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5 Activities to teach your students how to spot fake news

By CYPHER Learning

How to spot fake news ? These two words have trended in the last decade as a way of describing news and information that is false. It is not as simple as that, though. Other words like misinformation, disinformation, propaganda, satire, hoaxes, and conspiracy theories also describe something very similar and have been around for much longer. They do not, however, convey that snappy dismissive air conjured by the words “fake news.” To understand this trend, it is important to realize that there is no one description of what it is. In reality, fake news is three separate things:

• Stories that are not true (making people believe something entirely false);
• Stories that are partially true (a deliberate attempt to convince a reader of a viewpoint using skewed information or opinion);
• A tactic used to discredit other people’s views (to make another person’s opinion or even facts appear to someone else to be false, even when there is no sign that this is the case).

What all three descriptions have in common is the attempt to confuse and misdirect . As such, the tools that we need to teach our students are the same ones that we use to help them to assess information and conduct their own research for assignments.

Five activities to teach students how to spot fake news

Students must learn skills and capabilities to check the quality, bias, and background of news they encounter daily. Thus, when instructing students how to spot fake news, we need to teach them to:

• Develop a critical mindset (consider bias, quality, sensationalism, and the date of creation);
• Ask: why has this been written, and by who?;
• Check the source of the story;
• Check elsewhere to see if the story appears in more than one place.

With all of this in mind, how to approach this topic in the classroom? Here are five ideas that will help you navigate this challenging subject:

1. The News Comparison exercise

I love this one. Ask your students to select three or four national news websites. However, there’s a catch: they should include sites they would generally avoid because it conflicts with their opinions. Next, ask them to select the main news item of the day. Visit each of the websites and compare the article they have written to one another.

What is different? What is similar? Your students can write a short reflection about what they have found and then discuss how they might feel about the topic if only one of these sites was their sole source of news. You might wish to take this one step further by asking them to “fact-check” the news story using one of the fact-checking websites such as FactCheck.org , Snopes.com , Washington Post Fact Checker , Politifact.com . While not specifically about fake news, this exercise helps students understand the nature of news and the variability and quality of the information found online.

Read more: Digital reflection tools your students can use in class

2. Google Reverse Image Search

Images are just as likely as text to have been falsified or altered. Set your students a task to trace the history of an image through Google Reverse Image Search:

• Right-click on an image on a website and copy the image address or select an image on your hard drive;
• Go to Google Reverse Image Search ;
• Click on the camera icon and then paste the image address URL into the search field or upload your image from your hard drive;
• The results will show you where the image has appeared online.

This allows you to see where that image has appeared online (context) and to see similar images (which might reveal that it has been doctored).

3. LMS Quiz

Another way to engage students with issues around fake news is to develop a quiz on your learning management system (LMS) which asks students to spot fake news. Here are a few sample questions you might wish to use:

Q: Is this a photograph of how MGM created the legendary MGM intro of a lion roaring?

A. Show students the MGM version then reveal the real version , which is a picture from 2005 of a lion receiving a CAT scan. You can find plenty of other examples online .

Q: In the lead-up to the 2016 US Presidential election, Pope Francis broke papal tradition by endorsing the US Presidential candidate Donald Trump. True or False?

A: False. This fake story appeared on the now-defunct website WTOE 5 News and spread from there. Reuters and other reputable news sources confirmed that the news was false. See the fact check on this story

Q: NASA plans to install internet on the Moon. True or False?

A: True. NASA plans to build a 4G network on the moon to help them control lunar robots. This is called LunaNet. Find out more on the NASA website and see one of the news stories on CNBC .

Read more: 9 Types of assignments teachers can create in their LMS to evaluate student progress

4. Discuss research about fake news

Scholars have published articles about fake news in recent years, examples include Apuke and Omar (2021), Tsfati et al. (2020), and Leeder (2019). Select one or two articles for students to read and appraise, and then discuss the points raised in the classroom, asking them questions about how to spot fake news and websites.

As a homework assignment, ask students to investigate a current fake news story and compare their findings to the research. You might ask them to upload a brief response on an LMS forum, blog, or digital portfolio as an additional exercise. Or, perhaps, to create a poster to advertise to their peers why they should not fall for it.

5. Make up a fake news story

This is a fun exercise to do as a lesson to spot fake news. Divide your class into two groups:

Group A: write a fake news story;

Group B: write a real story.

Ensure that they are unable to share which group they are in. Ask them to individually write a short 500-word news story and then post it to the LMS forum or blog. Students in Group A should make up the story but add three elements of truth to it. Students in Group B should write about a real story that they find online from a reputable source (but one that is on a niche topic). Once done, divide your class into different small groups, and ask them to read through the stories, discuss, and label each one as Fake News or Real. Bring the class together to discuss the results and ask each student to update their forum/blog post to identify it as Fake or Real.

Learning how to spot fake news is a crucial skill

Fake News is a real challenge for educators. However, by teaching research skills to students, it becomes easier for them to identify misinformation and assess the quality of their sources. If there is one more piece of advice I would offer it is to make these activities as real as possible for students. Let them discover information themselves using the tools that they would normally use and focus on how they share stories.

In addition to the activities listed above, you might wish to try exercises created by Noah Tavlin , Vicki Davis , or Terry Heick . In addition, SFU Library provides a nice infographic and some videos about Fake News. Mindtools also provide some useful examples.

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Join the community, add a new evaluation result row, fake news detection.

166 papers with code • 9 benchmarks • 29 datasets

Fake News Detection is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake. The goal of fake news detection is to develop algorithms that can automatically identify and flag fake news articles, which can be used to combat misinformation and promote the dissemination of accurate information.

Benchmarks Add a Result

--> --> --> --> --> --> --> --> --> -->

Trend	Dataset	Best Model	Paper	Code	Compare
		Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021)
		Text-Transformers + Five-fold five model cross-validation +Pseudo Label Algorithm
		Hybrid CNNs (Text + All)
		Auxiliary IndicBert
		Ensemble Model + Heuristic Post-Processing
		TextRNN
		SEMI-FND
		SEMI-FND
		Convolutional Tsetlin Machine

Most implemented papers

"liar, liar pants on fire": a new benchmark dataset for fake news detection.

In this paper, we present liar: a new, publicly available dataset for fake news detection.

Fake News Detection on Social Media: A Data Mining Perspective

KaiDMML/FakeNewsNet • 7 Aug 2017

First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination.

Explainable Tsetlin Machine framework for fake news detection with credibility score assessment

cair/TsetlinMachine • LREC 2022

The proliferation of fake news, i. e., news intentionally spread for misinformation, poses a threat to individuals and society.

Fake News Detection on Social Media using Geometric Deep Learning

One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.

Defending Against Neural Fake News

We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data.

r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection

entitize/fakeddit • 10 Nov 2019

We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.

TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation

Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.

CSI: A Hybrid Deep Model for Fake News Detection

sungyongs/CSI-Code • 20 Mar 2017

Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.

``Liar, Liar Pants on Fire'': A New Benchmark Dataset for Fake News Detection

In this paper, we present LIAR: a new, publicly available dataset for fake news detection.

FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network

This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance.

6 Ways To Identify Fake News: A Complete Guide for Educators

This guide is designed to provide teachers and students with a clear understanding of how to identify fake news, how it can harm our society, and how to identify and respond to it.

We also have a guide on Critical Thinking Skills around Fake News for teachers and students, including teaching strategies and lesson plans.

What is Fake News?

Fake news can be described as “ False or misleading content presented as news and communicated in formats spanning spoken, written, printed, electronic, and digital communication .”

Nolan Higdon, Media Scholar

Despite popular opinion, the term Fake News has existed for a while. Though it certainly has become something of a buzzword in recent years. Gone are the days when we all get our news exclusively from longstanding newspapers and a handful of television channels. 

The power of broadcasting information is now in everyone’s hands, thanks to social media platforms such as Facebook, Twitter, Instagram, and YouTube.

While this has greatly ‘democratized’ the sharing of information, it has also thrown up some authentic problems that we must help our students to navigate, for example:

• How can I spot fake news from actual news?
• How do I know if a source of information is credible?
• What is ‘clickbait’, and how can I recognize it?
• What is propaganda, and how can I identify it?

The first step to spotting fake news is to define clearly what is meant by the term itself. Unfortunately, this has been made all the more difficult as ‘fake news’ has become a convenient slur used by one side to cast doubt on the claims of their political opponents.

The skill of how to spot fake news has rapidly evolved from an academic skill to a life skill.

A Complete Teaching Unit on Fake News

Digital and social media have completely redefined the media landscape, making it difficult for students to identify FACTS AND OPINIONS covering:

Teach them to FIGHT FAKE NEWS with this COMPLETE 42 PAGE UNIT. No preparation is required,

Consequences of Fake News

Fake news has significant consequences, leading to misinformation, division, and erosion of trust in institutions. It can sway public opinion, influence elections, and incite violence.

Education is essential in an era of digital media saturation, where misinformation spreads rapidly through social networks. Media literacy empowers individuals to critically evaluate information, discern credible sources, and navigate the complexities of the digital landscape.

By teaching students to question, analyze, and verify news sources, educators equip them with the tools to effectively combat fake news. Without education in media literacy, individuals are more susceptible to manipulation, polarization, and the harmful effects of misinformation. Promoting critical thinking skills and responsible online behaviour fosters informed citizens capable of navigating the modern information age with discernment and integrity.

Types of Fake News

To begin the process of spotting fake news, students need to understand there are three main types to recognize:

• False Stories: Though these stories may dress in the clothes of news, they are entirely fabricated. They are usually invented to sell a particular product, entice the reader to visit a specific website, or even mislead the reader into believing something false.
• Half-Truths: These are usually much more difficult to spot as they contain elements of truth mixed among falsehoods and misrepresentations. For example, a journalist might quote a source accurately but deliberately neglect to provide important context to what was said.
• Clickbait: The purpose of clickbait is solely to get readers to click a link. Misleading headlines that don’t accurately reflect the article’s content are often used. The clicks create ad revenue for the site owner. Clickbait is usually easy to recognize due to its overreliance on sensationalism to gain the reader’s attention.
• Biased Reporting: When a story is covered but done so that the reader misses balanced information on the topic, this can be considered biased. Deliberately leaving one side of a story out or overemphasizing the other will generally lead to a biased news story told from one deliberate perspective.

Regardless of the type, fake news will always mimic the appearance of news but will lack the verifiable facts, credible sources, and objectivity that is the mark of real news.

In the face of such convincing fraud, our students must be trained to evaluate news sources to accurately distinguish the reliable and the fair-minded from the phony and one-sided. 

In the remainder of this article, we’ll examine six practical strategies to help students do just that. We’ll also look at several online tools students can use to assist them in their fake news detection efforts.

Examples of Fake News

• False Stories
• Half-Truths
• Biased Reporting

Strategies for Identifying Fake News

Develop a critical mindset.

Developing a critical mindset is the first and most important aspect of learning how to spot fake news. 

This active skill requires students to engage their rational and reflective minds whenever they read or hear something. To do this, they must ask questions – and lots of them!

Students will initially need to ask questions about the things they see or hear consciously. With practice, however, they will instinctively rigorously question the messages they are exposed to.

They must develop a systematic approach to help students make critical thinking a habit. Encourage your students to ask the following questions when they encounter a new source (you may even like to make a display of these questions for your classroom):

• Who said it?
• What did they say?
• Where did they say it?
• When did they say it?
• Why did they say it?
• How did they say it?

Each of these questions provides a good starting point that will allow students to dig deeper into the integrity of a source and ask further follow-up questions.

Check the Source and Publisher

Whether the student wishes to check the site an article is hosted on or a site linked to as a supporting source, the following method applies.

First, the student should look at the URL address and who owns it. 

Is the website from a reputable organization or an established institution?  

One way to help assess this is to look at the domain suffix (the last part of the web address), as not all domain suffixes are created equal.

For example, the popular .com ending usually denotes a commercial site. While this does not automatically mean the information the site contains is unreliable, it is helpful to know the site’s primary purpose is to sell goods or services when weighing up the reliability of the information.

On the other hand, educational institutions use the domain ending .edu. Again, this doesn’t guarantee the reliability of the content, but it can be a starting point for further investigation. Students should dig deeper. For example, if the source is from a research centre at an educational institution, the student is most likely dealing with a reliable source. 

Encourage vigilance, though. Sometimes, students can host blogs on institutional websites. These personal blogs frequently contain opinion-based information that isn’t necessarily subject to the rigorous peer review process that research generally undergoes.

To learn more about the institution that owns the website, students should look at the About Us tab and related tabs such as Our Mission , Aims , Vision , etc. This may give some helpful information on which students can base their evaluation.

Two other important domain suffixes for students to recognize are .gov and .org .

.gov websites are official government sources, while .org used to be exclusively for non-profit organizations. However, sometimes, such organizations are sponsored by commercial entities.

Once the students have looked at the site publisher, they should investigate the author. 

Who are they? What are their credentials in this area? 

A simple online search of an author’s name often reveals lots of helpful information for students evaluating their value as a source.

Cross Reference With Other Sources

Another way to gauge the validity of a news story from a particular source is to cross-reference it with other trusted sources. 

For example, can the student find the same story reported by respected global news companies? 

If the only place the story appears is on a dubious website with clear commercial or political ends, then the account is much more likely to be fake news.

Carefully listening to or reading a news story can often reveal opportunities to check the story out. 

For example, what are local media outlets saying about what happens if an oil spill is reported off the coast?

When news is reliable, it will most likely be possible to confirm it through several other reputable news sources.

Gather Your Own Evidence

Cross-referencing stories with other news reports isn’t the only way to find validity evidence. Usually, the news will contain other specific types of evidence that can be checked individually.

Can other sources confirm the interviews and quotes in the story? Is there video or audio footage, for example? 

Students should seek out supporting (or contradictory) sources and weigh up the news report in light of what they uncover.

How about surveys and statistics? Do the numbers confirm what has been reported?

Here, students need to be careful, too. The careful selection of numbers can be used to prove almost anything! As a British statesman once said, “There are three kinds of falsehoods, lies, damn lies, and statistics!”

Ensure Your Information is current.

We live in the age of the 24-hour news cycle. Unlike a bygone era, when printing and broadcast schedules allowed for time to edit, fact-check, and amend news before publishing, news can reach a global audience instantly at the push of a button.

On the other hand, whereas TV broadcasts and daily newspapers are somewhat disposable, news articles published online can remain permanently in the virtual world.

Often, they are published and forgotten about. Though new facts may subsequently come to light, these articles aren’t updated. To ensure students are up-to-date with the news, they should always check the date the article was published. The date of publication is often printed just under the article’s title.

With the widespread use of social media, old news articles are frequently reposted and reshared, often without explicitly stating the news is ‘old’ or has been updated since the original publication. 

In these instances, the onus is on the student to check when the article was published and if the story has been modified or updated since that original publication.

Always ask the Experts

Another approach to spotting fake news is outsourcing it to experts in the field. These experts can come in many different forms. For example, professors, librarians, researchers, scientists, and journalists can all represent authoritative voices in specific areas.

In recent years, many fact-checking websites have also popped up. These websites present themselves as impartial arbiters of factual accuracy and objectivity, usually employing a rating system to evaluate stories of the day doing the rounds.

As with any of the strategies outlined in this article, the ‘ ask the experts ’ strategy is far from perfect. While every fact-checking website presents as completely unbiased and objective, they do not always get things right. 

Detecting bias can be a subjective pursuit that can sometimes say as much about the checker’s bias as it does about the checked. Students must still focus on the material and keep their critical faculties engaged.

Identify Fake News With These Online Tools

Here are a few of the best-known fact-checking websites that students can use:

Media Bias/Fact Check This website contains a database of over 3,700 media sources it rates according to a variety of criteria, including bias, political leaning, and an assessment of it tendency for factual reporting.

Hoaxy Run by Indiana University Bloomington, Hoaxy aims to track the spread of misinformation online by tracking the sharing of articles from low-credibility sources on social media.

Snopes originally started as a website devoted to debunking urban myths, but Snopes now also focuses on American politics and has a left-of-centre political leaning itself.

FactCheck.org This website fact-checks statements made by major political figures (predominately US). The nonprofit Annenberg Public Policy Center runs it.

PolitiFact : PolitiFact rates the accuracy of claims by elected officials and others who speak up in American politics. They use a “Truth-O-Meter” to rate statements on a scale from “True” to “Pants on Fire.”

Google Fact Check Explorer: Google’s Fact Check Explorer allows users to search for specific claims or topics and see fact-checking articles from various sources.

Full Fact : Full Fact is an independent fact-checking organization based in the UK. They fact-check claims made by politicians, public institutions, and the media.

The Washington Post Fact Checker : This project of The Washington Post evaluates the accuracy of claims made by politicians and other public figures.

OpenSecrets.org : This website tracks money’s influence on U.S. politics and public policy. It provides information on campaign donations and lobbying efforts, allowing students to verify claims about political contributions.

The Pacific Northwest Tree Octopus is an excellent resource for students to be exposed to before exploring fake news. It is a carefully crafted site about the extremely rare “Pacific Northwest Tree Octopus” and what we can all do to save it from extinction before it’s too late. Most students will easily fall for this trap; it is a great conversation starter.

Theonion.com is an excellent example of a parody media platform that adds fresh content daily. It is clever, witty, and sometimes wholly and deliberately misleading. Some content can sometimes sit on the edge of risque, so pick your audience when using it.

Here is a list of hundreds of fake news sites broken into various categories. Please take the time to explore these yourself before freely sharing them with students, as they obviously contain misleading and commonly inappropriate information. You will easily find numerous examples of current fake news in action here.

Your Local Library : Libraries often provide access to databases and resources that can help students fact-check information, including academic journals, newspapers, and reference materials.

Encouraging students to use these tools can help them develop strong critical thinking skills and ensure they rely on accurate information in their research and studies.

Media Literacy Versus Fake News

Media literacy is crucial in combating fake news. It equips individuals with the skills to critically evaluate information, discern credible sources, and recognize misinformation tactics. Through media literacy education, people learn to question the motives behind news stories, analyze biases, and verify information from multiple reliable sources.

By understanding how news is produced, distributed, and consumed, individuals become less susceptible to manipulation and misinformation.

Media literacy fosters a sceptical mindset, encouraging people to scrutinize headlines, examine the evidence, and challenge false narratives. Moreover, media literacy promotes responsible online behaviour, emphasizing the importance of fact-checking before sharing information.

In an age where misinformation spreads rapidly through social media and digital platforms, media literacy empowers individuals to navigate the complex information landscape with discernment and integrity. It enables citizens to make informed decisions, participate meaningfully in democratic processes, and uphold the integrity of public discourse. Be sure to read our guide to teaching Media Literacy here.

Unfortunately, the speed with which these new technologies have developed has outstripped our ability to assess what we see on these platforms critically. We are playing catch-up.

As most of our students are daily users of social media, opportunities to put their fake news detection skills to practice won’t be difficult to come by. 

A good starting point for internalizing the above strategies is to tell your students that from now on, before sharing any material online, they should check every source thoroughly to ensure it is true. Disturbingly, 6 out of 10 happily share articles online that they haven’t read themselves. No fake news would ever go viral if everyone did a little fact-checking before sharing.

One of the most challenging aspects of teaching fake news detection skills to our students is that it requires legwork. Separating fact from fake with precision takes effort. Remind students that it is worth the effort. Uncovering the truth is rewarding and helps make the world a better place. Not to mention, no one likes to be duped or used by others for nefarious ends.

With time and practice, the strategies above will become second nature as students develop an instinct for identifying the misleading and the downright fraudulent. 

However, students should also learn that they don’t need to make snap judgments about a source. They should be encouraged to habitually suspend their judgment until they’ve had a chance to examine the evidence. They must develop a negative capability to deal with uncertainty until they’ve had a chance to evaluate sources adequately.

Finally, the students should be impressed that while the above strategies are all beneficial in their own right, they aren’t foolproof. The best chance for students to accurately identify fake news is to use these different strategies in conjunction with each other and constantly apply their own judgment.

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Detecting Fake News

Minilesson print.

“Fake news” refers to intentionally false, misleading, or exaggerated stories disguised as factual news. Such stories can appear in any medium but appear frequently on social media, where misinformation can be shared widely and rapidly.

Fake news may be difficult to spot, so you need to be on high alert when you view news from your social accounts. In the following activity, you'll apply critical questions to help you separate real news from fake news.

Your Turn Follow the directions to detect fake news.

• Choose a news story that you don't know much about, preferably one that is shared on one of your social-media feeds. (If you don't use social media, type "trending topics" into a search engine, and choose a story from the results.)
• Apply the "Critical Questions for Spotting Fake News" to the story. (You can download the questions at the bottom of the lesson.)
• Write a brief paragraph that explains why the story is or is not reliable. In your response, identify at least one other source that confirms (or debunks) the information.

From the blog post " Lessons for Combating Fake News "

Teacher Support:

Click to find out more about this resource.

Answers will vary.

Standards Correlations:

The State Standards provide a way to evaluate your students' performance.

© 2024 Thoughtful Learning. Copying is permitted.

k12.thoughtfullearning.com

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Evaluating Sources in a ‘Post-Truth’ World: Ideas for Teaching and Learning About Fake News

By Katherine Schulten and Amanda Christy Brown

• Jan. 19, 2017

Back in 2015, when we published our lesson plan Fake News vs. Real News: Determining the Reliability of Sources , we had no way of knowing that, a year later, the Oxford Dictionaries would declare “post-truth” the 2016 word of the year; that fake news would play a role in the 2016 presidential election; that it would cause real violence ; and that the president-elect of the United States would use the term to condemn mainstream media outlets he opposes.

Back then, to convince teachers that the skill was important, we quoted Peter Adams of the News Literacy Project on the “digital naïveté” of the “digital natives” we teach. Now, however, we doubt that we need to convince anyone.

These days, invented stories created in a “fake news factory ”— or by a 23-year-old in need of cash — go viral, while articles from traditional sources like The Times are called “fake news” by those who see them as hostile to their agenda .

That, writes Sabrina Tavernise in “ As Fake News Spreads Lies, More Readers Shrug at the Truth ,” leads to an insidious problem:

Fake news, and the proliferation of raw opinion that passes for news, is creating confusion, punching holes in what is true, causing a kind of fun-house effect that leaves the reader doubting everything, including real news.

In this lesson, we update our 2015 post with new resources for helping your students navigate this uneasy landscape. Divided into two sections — The Problems and The Possible Solutions — it offers practical activities and questions throughout. (Update: We also now have a companion lesson for E.L.L. students .)

As always, we welcome your ideas; please post them in the comments.

The Problems

Why Does This Matter? Framing the Problem for Students:

First, have your students look at the image below. Ask them, “Does this provide strong evidence about the conditions near the Fukushima Daiichi Nuclear Power Plant? Why or why not?”

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Fact Check: How to decipher online news and information: Identifying Fake News

Types misinformation, astroturfing.

Organizations or sponsors (political, religious, etc) make the message they are sharing look like it is from a grassroots organization and supported by people in the community where they are targeting the messages.

News is delivered from a particular point of view that may rely on propaganda and opinions rather than facts.

Links use sensationalized, misleading, or exaggerated headlines and images to get individuals to visit their website.  The articles then deliver information that is not related to the original eye-catching piece.  

This is a genuine mistake made by a reporting agency. Once the error is found, reputable agencies will retract the story and publish an apology if necessary

Native advertising/Sponsored Content

Native advertisements are designed to look like additional stories but are advertisements for sponsors. Readers mistake their links as legitimate news to get more traffic to their site. 

Appeals to emotions and used to manage attitudes towards a government or corporation. This type of information can be both beneficial and harmful

News sites parody actual events and news and are for entertainment purposes only.  They often mimic reputable news sites, using exaggerated information out of context. 

Types of Disinformation

Conspiracy theories.

Fictional claims that reject experts and authorities. Claims cannot be falsified, any evidence that refutes the theory is considered additional support for the conspiracy

Counterfeit

Social media accounts purporting to be a certain person but are, in fact, run by others with no connection to that individual.

Doctored content

Content within a document has been modified or altered. This can include statistics, graphs, photographs, and videos.

Websites have made up stories or hoaxes that are delivered under the pretext of being factual news.  

False attribution

Genuine images, videos, or quotes are knowingly attributed to the wrong person or event.

News stories share quotes or information without providing proper background or context, which can often completely reverse the intended message.

Pseudoscience

Often contradicts experts with no evidence to support their claims. Misrepresent real scientific studies with exaggerated or false claims

How to spot fake news

Text alternative to How to Spot Fake News Infographic.

International Federation of Library Associations and Institutions. (2020).  How to spot fake news  [Infographic].  https://www.ifla.org/publications/node/11174

IFLA, CC BY 4.0 <https://creativecommons.org/licenses/by/4.0>, via Wikimedia Commons 

Questions to ask yourself to identify fake news

Does the story match the headline   .

Sensational headlines are created to get the reader's attention. They often contain excessive punctuation, such as exclamation points, and use all caps. Sometimes they allude to a secret you need to know now. Keep reading! If the story strays away from the headline, it is probably clickbait. Clickbait is used to pass biased or fake news to unsuspecting readers.    

What is the date of the story?  

Some deceptive sites take stories or pictures from a few years ago and revamp them to fit in a headline with today's date. There are resources in this guide to help you verify images to find when they first appeared on the internet.   

Is the story unbelievable?  

If it's so incredible that you cannot believe it, you shouldn't.  Alternatively, if it confirms your worst nightmare, you should research it deeper to find supporting or contradictory evidence.    

Who else is reporting this story?  

 If no other media outlets are reporting the same story, it may not be true. Look for supporting evidence and links to reputable news outlets.    

Who is reporting this story?   

Are they a reputable news agency? Look up the author to see what and where they have reported before. Check the website's About page. Be careful of sites that do not provide information on who they are or how to contact them. Reputable sites will have their contact information readily available that should match the domain, not a Yahoo or Gmail address. All media is vulnerable to mistaken facts and news.  However, reputable news sources take accountability for their stories.  Biased and fake news outlets often do not take the same steps toward accountability, even going as far as giving fake contact information  

How did you find this story?  

Is it from your over-sharing aunt on social media? Did it come across your newsfeed? Is it a meme? Many people who share stories on social media do not read past the headline. Reputable news outlets will not share newsworthy stories in a meme. Look the story up elsewhere to see if anyone is reporting on it and what they are saying. If it is a link to a website, check out the URL. Some fake websites create fake websites that look like other news agencies. Look for inconsistencies in the URL, such as spelling errors.   

Does the article make statements without any supporting evidence?  

 You should always be able to see where the information in the story came from, such as links to original articles and named sources. Look to see if they are trying to prove or disprove something based on only one encompassing fact and treat it as a warning sign. You should also look up the report on a fact-checking site, such as Snopes or FactCheck.org.  

Does the article show opposing viewpoints?  

Did the author take steps to get any information to tell the other side of the story?  This demonstrates their credibility and transparency in bringing you the whole story. Search other media outlets to see what they are saying about this story.     

Knowledge check

Text alternative to how to spot fake news infographic, how to spot fake news  , consider the source.

Click away from the story to investigate the site, its mission, and its contact info.

Read beyond

Headlines can be outrageous in an effort to get clicks. What’s the whole story?

Check the author

Do a quick search on the author. Are they credible? Are they real?

Supporting sources?

Click on those links. Determine if the info given actually supports the story.

Check the date

Reposting old news stories doesn’t mean they’re relevant to current events.

Is it a joke?

If it is too outlandish, it might be satire. Research the site and author to be sure.

Check your biases

Consider if your own beliefs could affect your judgment.

Ask the experts

Ask a librarian, or consult a fact-checking site.

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What Are You Searching For?

Using machine learning to detect fake news.

Since the 2016 presidential election, awareness of fake news has soared. Detecting and preventing the spread of unreliable media content is a difficult problem, especially given the rate at which news can spread online. With its power to erode the public's ability to make informed decisions, fake news poses a serious threat to our national security.

To combat this threat, the Lincoln Laboratory Technology Office hosted the "Fake Media" Hackathon from 8 to 9 June. The hackathon, which was the first-ever organized at the Laboratory, challenged teams of staff to use machine learning to automatically detect fake media content. The effort wrapped up with post-hack presentations on 28 June, when the three top-scoring teams and overall challenge winner were announced.

"Fake news is definitely a hot, if controversial, topic right now. Inside and outside of the Laboratory, people are very enthusiastic about doing their part to counteract the influence of false or misleading information online," said Elizabeth Godoy, a member of the Human Language Technology Group at Lincoln Laboratory. Godoy organized the hackathon with fellow group member Charlie Dagli and Deborah Campbell, Associate Technology Officer.

Lincoln Laboratory has really jump-started technology development to combat fake news.

Forty-five participants, including staff members and interns, from across the Laboratory's divisions signed up for the challenge. The challenge organizers split participants into nine teams. During the month prior to the hackathon, teams prepared and strategized using example data—images, text, and html metadata drawn from 1600 truth-marked articles. The teams were also provided with baseline algorithms to begin developing systems. The core task of the challenge was to train systems to extract features from the data, classify the features, and fuse those classifications into a binary decision: reliable or unreliable.

"To solve the fake media challenge problem, the team must develop tools that consider all aspects of the data," said Lin Li, a staff member in the Human Language Technology Group and a captain of the winning team, Tor News Network. Teams created a variety of tools to find features in the data that could help determine if the content was fake. Tools included stance classification to determine whether a headline agreed with the article body, text processing to analyze the author's writing style, and image forensics to detect Photoshop use. Algorithms to extract even relatively simple data features, like image size, readability level, and the ratio of reactions versus shares on Facebook, proved useful in determining article reliability.

On the first day of the hackathon, the teams were given the official challenge data to put their systems to the test. Dagli led the data collection effort with the Laboratory's Open Source Data Initiative, building a dataset from annotated news websites that included more than 12,000 articles published within a two-week period in May. "It was very important for us to make sure we collected real-world data. This meant a lot of up-front work, but led to a meaningful dataset, and more importantly, realistic technical approaches," Dagli said.

Each team was allowed to submit their detection results up to five times. The organizers scored each test submission based on its rate of true detection versus false detection (i.e., the receiver operating characteristic [ROC] curve). An area under the curve (AUC) of 1.0 represents a perfect test. All teams delivered impressive results, with the top three scores clustered around .975 AUC. In between submissions, teams went to work tweaking their systems to improve their score. "A few of the handcrafted features that we introduced were surprisingly effective, improving our classifier performance by about 10%," said Cem Sahin, a top-scoring-team captain from the Cyber Analytics and Decision Systems Group.

While teams aimed to outperform each other, collaboration was highly encouraged and included in the evaluation criteria used to select the challenge winner. Participants shared their tools, troubleshooting tips, and limited time with other teams during the hackathon. Staff were also encouraged to have fun, especially with team names. #SAD (Suspicious Article Detection) was voted the best name at the post-hackathon event.

During final presentations, teams shared their successes. Many expressed how much they learned in such a short period of time. "The time constraints, both during the preparation phase and during the actual hackathon, were definitely the most challenging aspects for me," said Olga Simek, a staff member from the Intelligence and Decision Technologies Group whose team placed third. "It was a great experience. Both the subject-matter experts and the team members new to the subject area learned a lot and really enjoyed the interactions with other participants."

While the challenge has concluded, it is likely that these efforts will continue. "From professors on MIT campus to industry leaders, we have been receiving a lot of requests to access our data and collaborate on different aspects of the problem," Godoy said. "Lincoln Laboratory has really jump-started technology development to combat fake news."

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Fake News, Misleading News, Biased News: Assignments on Evaluating Sources

• Evaluating Sources
• Assignments on Evaluating Sources
• Terms and Definitions
• Open Textbooks
• Fact-Checking Sites and Plug-Ins
• Coronavirus COVID-19
• Fake News and AI

Assignments

• Caulfield, Mike. The Four Moves: Adventures in Fact-Checking for Students
• CORA (Community of Online Research Assignments). Evaluating news sites: Credible or Clickbait?
• McCormick Foundation. Introduction to news literacy: Structured engagement with current and controversial issues.
• University of Delaware. Curing fake news phobia (Google Doc with lesson plan - by Lauren Wallis)
• University of Texas El Paso. News gathering and investigation: An evaluation exercise
• Whiting, Jacquelyn. (2019, September 4). Everyone has invisible bias. This lesson shows students how to recognize it. EdSurge.

More Assignments

C-SPAN Classroom: Lesson idea: Media Literacy and Fake News

SchoolJournalism.com   News  and media literacy lessons.

Walsh-Moorman, Elizabeth and Katie Ours. Introducing lateral reading before research MLA Style Center. (Objectives include identifying credibitilty and/or bias of a course, identifying how professional fact-checkers assess iinformation vs a general audience.)

• The Media Manipulation Casebook Includes methods, definitions of terms related to misinformaiton, disinformation, and media manipulation.

A Course on News Literacy

Making Sense of the News: News Literacy Lessons for Digital Citizens   A six week course offered by The University of Hong Kong & The State University of New York via Coursera,  Audit the course  for free. Resources include a glossary of terms such as bias, cognitive dissonance, confirmation bias,  propaganda, selective dissonance, verfication, etc.  

News Literacy. Digital Resource Center. Stony Brook University

Stony Brook University. Digital Resources Center.   The 14 Lessons   This course pack consists of lessons that can be taught in sequence or separately and cover topics such as verification, fairness and balance, bias, etc. This material is the basis for the Coursera course (above) on news literacy.

Fake New: Curriculum. Cal State University Long Beach

• Fake News: Curriculum Curriculum about fake news, curriculum guides, presentations; instructional strategies from librarian Lesley Farmer.

Fake News in First-Year Writing - Paul Corrigan

• Corrigan, Paul T. Fake News in First-Year Writing. Writing Commons.org A description of a first-year writing course that integrates feeling and fact-checking with a description of the writing projects.

Need to Evaluate a Source? Try a Worksheet

• Evaluating Web Sites: A Checklist (University of Maryland)

Quality of News Sources - You Decide!

Vanessa Otero - a patent attorney - made a chart with her views on various news sites - and you can too! She put out a blank version so you can decide. See her blog post on  news quality   and her chart on Twitter  

Valenza, J.  (2016, November 26).   Truth, truthiness, triangulation: A news literacy toolkit for a "post-truth" world.   School Library Journal.  

A course from University of Washington, Seattle, WA

• Calling Bullshit - Course Syllabus A proposal for a course by two professors from University of Washington, Seattle meant to teach students how to recognize. bullshit. "Bullshit is language, statistical figures, data graphics, and other forms of presentation intended to persuade by impressing and overwhelming a reader or listener, with a blatant disregard for truth and logical coherence."
• Check, Please! Starter Course "In this course, we show you how to fact and source-check in five easy lessons, taking about 30 minutes apiece. The entire online curriculum is two and a half to three hours and is suitable homework for the first week of a college-level module on disinformation or online information literacy, or the first few weeks of a course if assigned with other discipline focused homework." This course has been released into the public domain.
• Real vs. Fake. Science vs. Pseudoscience. A course syllabus Fall 2019 A course by Dr. Douglas Duncan, University of Colorado Boulder

A course from University of Michigan on fake news

• Fake News, Lles, and Propaganda: The Class An entire seven week course developed by librarians at the University of Michigan.

Learning Tools Suggested by Richard Byrne

Learning tools suggested by Richard Byrne in his Practical Ed Tech Tip of the Week .

• Can You Spot the Problem with These Headlines? A TED-Ed lesson.
• Checkology: A free version with interactive modules (that become increasingly difficult.)
• Civic Online Reasoning: Lesson plans From the Stanford History Education Group. (SHEG). Lessons on lateral reading with fact-checking organizations, who's behind the information? What's the evidence? Create a free account to the SHEG to access these lessons.
• Spot the Troll A troll is a fake social media account, often created to spread misleading information.- Learn to spot them! From Clemson University's Media Forensics Lab
• This One Weird Trick Will Help You Spot Clickbait. A TED-Ed lesson
• << Previous: Evaluating Sources
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• Last Updated: Sep 4, 2024 4:18 PM
• URL: https://libguides.hccfl.edu/fakenews

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Kreitzberg Library for CGCS Students

Identify fake news and evaluate news sources.

• What Is Fake News?
• How to Recognize Fake News
• Fact Checking 101
• Know Your Sources
• Practice Spotting Fake News

Quick and Simple Debunking Exercise

Compare these two links. Which one do you think is true? Why or why not?

• Story #1 - " Shocking Facts About Farmed Salmon "
• Story #2 - " Farmed vs. Wild Salmon " 

Click here to submit your answer!

Source: IU East Campus Library - Fake News: Check Your Own Claim (2017)

Select a Claim to Examine

• How Food Companies Exploit Americans This article claims that US consumers ingest chemicals that people in other countries don't. Is that true? What else does this article imply?
• "Drinking Tequila Could Help You Lose Weight" Can you find enough evidence to prove or disprove this claim?
• "Ten Signs the Global Elite Are Losing Control" Lots of claims here. Pick one or two and check to your heart's content.
• "Marijuana Does Not Lower IQ" Up for the challenge of proving or disproving this one?

Source: IU East Campus Library - Fake News: Check Your Own Claim! (2017)

Can You Spot the Fake News?

• Quiz - Can You Spot the Fake News Story? Put your media literacy skills to the test with this interactive quiz from The Guardian.

What Makes "Real News" Real?

Adapted from IU East Campus Library - Check Your Own Claim! (2017)

• << Previous: Know Your Sources
• Last Updated: Aug 30, 2022 8:18 AM
• URL: https://guides.norwich.edu/online/fakenews

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Approaches to Identify Fake News: A Systematic Literature Review

Dylan de beer.

Department of Informatics, University of Pretoria, Pretoria, 0001 South Africa

Machdel Matthee

With the widespread dissemination of information via digital media platforms, it is of utmost importance for individuals and societies to be able to judge the credibility of it. Fake news is not a recent concept, but it is a commonly occurring phenomenon in current times. The consequence of fake news can range from being merely annoying to influencing and misleading societies or even nations. A variety of approaches exist to identify fake news. By conducting a systematic literature review, we identify the main approaches currently available to identify fake news and how these approaches can be applied in different situations. Some approaches are illustrated with a relevant example as well as the challenges and the appropriate context in which the specific approach can be used.

Introduction

Paskin ( 2018 : 254) defines fake news as “particular news articles that originate either on mainstream media (online or offline) or social media and have no factual basis, but are presented as facts and not satire”. The importance of combatting fake news is starkly illustrated during the current COVID-19 pandemic. Social networks are stepping up in using digital fake news detection tools and educating the public towards spotting fake news. At the time of writing, Facebook uses machine learning algorithms to identify false or sensational claims used in advertising for alternative cures, they place potential fake news articles lower in the news feed, and they provide users with tips on how to identify fake news themselves (Sparks and Frishberg 2020 ). Twitter ensures that searches on the virus result in credible articles and Instagram redirects anyone searching for information on the virus to a special message with credible information (Marr 2020 ).

These measures are possible because different approaches exist that assist the detection of fake news. For example, platforms based on machine learning use fake news from the biggest media outlets, to refine algorithms for identifying fake news (Macaulay 2018 ). Some approaches detect fake news by using metadata such as a comparison of release time of the article and timelines of spreading the article as well where the story spread (Macaulay 2018 ).

The purpose of this research paper is to, through a systematic literature review, categorize current approaches to contest the wide-ranging endemic of fake news.

The Evolution of Fake News and Fake News Detection

Fake news is not a new concept. Before the era of digital technology, it was spread through mainly yellow journalism with focus on sensational news such as crime, gossip, disasters and satirical news (Stein-Smith 2017 ). The prevalence of fake news relates to the availability of mass media digital tools (Schade 2019 ). Since anyone can publish articles via digital media platforms, online news articles include well researched pieces but also opinion-based arguments or simply false information (Burkhardt 2017 ). There is no custodian of credibility standards for information on these platforms making the spread of fake news possible. To make things worse, it is by no means straightforward telling the difference between real news and semi-true or false news (Pérez-Rosas et al. 2018 ).

The nature of social media makes it easy to spread fake news, as a user potentially sends fake news articles to friends, who then send it again to their friends and so on. Comments on fake news sometimes fuel its ‘credibility’ which can lead to rapid sharing resulting in further fake news (Albright 2017 ).

Social bots are also responsible for the spreading of fake news. Bots are sometimes used to target super-users by adding replies and mentions to posts. Humans are manipulated through these actions to share the fake news articles (Shao et al. 2018 ).

Clickbait is another tool encouraging the spread of fake news. Clickbait is an advertising tool used to get the attention of users. Sensational headlines or news are often used as clickbait that navigate the user to advertisements. More clicks on the advert means more money (Chen et al. 2015 a).

Fortunately, tools have been developed for detecting fake news. For example, a tool has been developed to identify fake news that spreads through social media through examining lexical choices that appear in headlines and other intense language structures (Chen et al. 2015 b). Another tool, developed to identify fake news on Twitter, has a component called the Twitter Crawler which collects and stores tweets in a database (Atodiresei et al. 2018 ). When a Twitter user wants to check the accuracy of the news found they can copy a link into this application after which the link will be processed for fake news detection. This process is built on an algorithm called the NER (Named Entity Recognition) (Atodiresei et al. 2018 ).

There are many available approaches to help the public to identify fake news and this paper aims to enhance understanding of these by categorizing these approaches as found in existing literature.

Research Method

Research objective.

The purpose of this paper is to categorize approaches used to identify fake news. In order to do this, a systematic literature review was done. This section presents the search terms that were used, the selection criteria and the source selection.

Search Terms

Specific search terms were used to enable the finding of relevant journal articles such as the following:

• (“what is fake news” OR “not genuine information” OR “counter fit news” OR “inaccurate report*” OR “forged (NEAR/2) news” OR “mislead* information” OR “false store*” OR “untrustworthy information” OR “hokes” OR “doubtful information” OR “incorrect detail*” OR “false news” OR “fake news” OR “false accusation*”)
• AND (“digital tool*” OR “digital approach” OR “automated tool*” OR “approach*” OR “programmed tool*” OR “digital gadget*” OR “digital device*” OR “digital machan*” OR “digital appliance*” OR “digital gizmo” OR “IS gadget*” OR “IS tool*” OR “IS machine*” OR “digital gear*” OR “information device*”)
• AND (“fake news detection” OR “approaches to identify fake news” OR “methods to identify fake news” OR “finding fake news” OR “ways to detect fake news”).

Selection Criteria

Inclusion criteria..

Studies that adhere to the following criteria: (1) studies published between 2008 and 2019; (2) studies found in English; (3) with main focus fake news on digital platforms; (4) articles that are published in IT journals or any technology related journal articles (e.g. computers in human behavior) as well as conference proceedings; (5) journal articles that are sited more than 10 times.

Exclusion Criteria.

Studies that adhered to the following criteria: (1) studies not presented in journal articles (e.g. in the form of a slide show or overhead presentation); (2) studies published, not relating to technology or IT; (3) articles on fake news but not the identification of it.

The search terms were used to find relevant articles on ProQuest, ScienceDirect, EBSCOhost and Google Scholar (seen here as ‘other sources’).

Flowchart of Search Process

Figure  1 below gives a flowchart of the search process: the identification of articles, the screening, the selection process and the number of the included articles.

A flowchart of the selection process

In this section of the article we list the categories of approaches that are used to identify fake news. We also discuss how the different approaches interlink with each other and how they can be used together to get a better result.

The following categories of approaches for fake news detection are proposed: (1) language approach, (2) topic-agnostic approach, (3) machine learning approach, (4) knowledge-based approach, (5) hybrid approach.

The five categories mentioned above are depicted in Fig.  2 below. Figure  2 shows the relationship between the different approaches. The sizes of the ellipses are proportional to the number of articles found (given as the percentage of total included articles) in the systematic literature review that refer to that approach.

Categories of fake news detection approaches resulting from the systematic literature review

The approaches are discussed in depth below with some examples for illustration purposes.

Language Approach

This approach focuses on the use of linguistics by a human or software program to detect fake news. Most of the people responsible for the spread of fake news have control over what their story is about, but they can often be exposed through the style of their language (Yang et al. 2018 ). The approach considers all the words in a sentence and letters in a word, how they are structured and how it fits together in a paragraph (Burkhardt 2017 ). The focus is therefore on grammar and syntax (Burkhardt 2017 ). There are currently three main methods that contribute to the language approach:

Bag of Words (BOW):

In this approach, each word in a paragraph is considered of equal importance and as independent entities (Burkhardt 2017 ). Individual words frequencies are analysed to find signs of misinformation. These representations are also called n-grams (Thota et al. 2018 ). This will ultimately help to identify patterns of word use and by investigating these patterns, misleading information can be identified. The bag of words model is not as practical because context is not considered when text is converted into numerical representations and the position of a word is not always taken into consideration (Potthast et al. 2017 ).

Semantic Analysis:

Chen et al. 2017 b explain that truthfulness can be determined by comparing personal experience (e.g. restaurant review) with a profile on the topic derived from similar articles. An honest writer will be more likely to make similar remarks about a topic than other truthful writers. Different compatibly scores are used in this approach.

Deep Syntax:

The deep syntax method is carried out through Probability Context Free Grammars (Stahl 2018 ). The Probability Context Free Grammars executes deep syntax tasks through parse trees that make Context Free Grammar analysis possible. Probabilistic Context Free Grammar is an extension of Context Free Grammars (Zhou and Zafarani 2018 ). Sentences are converted into a set of rewritten rules and these rules are used to analyse various syntax structures. The syntax can be compared to known structures or patterns of lies and can ultimately lead to telling the difference between fake news and real news (Burkhardt 2017 ).

Topic-Agnostic Approach

This category of approaches detect fake news by not considering the content of articles bur rather topic-agnostic features. The approach uses linguistic features and web mark-up capabilities to identify fake news (Castelo et al. 2019 ). Some examples of topic-agnostic features are 1) a large number of advertisements, 2) longer headlines with eye-catching phrases, 3) different text patterns from mainstream news to induce emotive responses 4) presence of an author name (Castelo et al. 2019 ; Horne and Adali 2017 ).

Machine Learning Approach

Machine learning algorithms can be used to identify fake news. This is achieved through using different types of training datasets to refine the algorithms. Datasets enables computer scientists to develop new machine learning approaches and techniques. Datasets are used to train the algorithms to identify fake news. How are these datasets created? One way is through crowdsourcing. Perez-Rosas et al. ( 2018 ) created a fake news data set by first collecting legitimate information on six different categories such as sports, business, entertainment, politics, technology and education (Pérez-Rosas et al. 2018 ). Crowdsourcing was then used and a task was set up which asked the workers to generate a false version of the news stories (Pérez-Rosas et al. 2018 ). Over 240 stories were collected and added to the fake news dataset.

A machine learning approach called the rumor identification framework has been developed that legitimizes signals of ambiguous posts so that a person can easily identify fake news (Sivasangari et al. 2018 ). The framework will alert people of posts that might be fake (Sivasangari et al. 2018 ). The framework is built to combat fake tweets on Twitter and focuses on four main areas; the metadata of tweets, the source of the tweet; the date and area of the tweet, where and when the tweet was developed (Sivasangari et al. 2018 ). By studying these four parts of the tweet the framework can be implemented to check the accuracy of the information and to separate the real from the fake (Sivasangari et al. 2018 ). Supporting this framework, the spread of gossip is collected to create datasets with the use of a Twitter Streaming API (Sivasangari et al. 2018 ).

Twitter has developed a possible solution to identify and prevent the spread of misleading information through fake accounts, likes and comments (Atodiresei et al. 2018 ) - the Twitter crawler, a machine learning approach works by collecting tweets and adding them to a database, making comparison between different tweets possible.

Knowledge Based Approach

Recent studies argue for the integration of machine learning and knowledge engineering to detect fake news. The challenging problem with some of these fact checking methods is the speed at which fake news spreads on social media. Microblogging platforms such as Twitter causes small pieces of false information to spread very quickly to a large number of people (Qazvinian et al. 2011 ). The knowledge-based approach aims at using sources that are external to verify if the news is fake or real and to identify the news before the spread thereof becomes quicker. There are three main categories; (1) Expert Oriented Fact Checking, (2) Computational Oriented Fact Checking, (3) Crowd Sourcing Oriented Fact Checking (Ahmed et al. 2019 ).

Expert Oriented Fact Checking.

With expert oriented fact checking it is necessary to analyze and examine data and documents carefully (Ahmed et al. 2019 ). Expert-oriented fact-checking requires professionals to evaluate the accuracy of the news manually through research and other studies on the specific claim. Fact checking is the process of assigning certainty to a specific element by comparing the accuracy of the text to another which has previously been fact checked (Vlachos and Riedel 2014 ).

Computational Oriented Fact Checking.

The purpose of computational oriented fact checking is to administer users with an automated fact-checking process that is able to identify if a specific piece of news is true or false (Ahmed et al. 2019 ). An example of computational oriented fact checking is knowledge graphs and open web sources that are based on practical referencing to help distinguish between real and fake news (Ahmed et al. 2019 ). A recent tool called the ClaimBuster has been developed and is an example of how fact checking can automatically identify fake news (Hassan et al. 2017 ). This tool makes use of machine learning techniques combined with natural language processing and a variety of database queries. It analyses context on social media, interviews and speeches in real time to determine ‘facts’ and compares it with a repository that contains verified facts and delivers it to the reader (Hassan et al. 2017 ).

Crowd Sourcing Oriented.

Crowdsourcing gives the opportunity for a group of people to make a collective decision through examining the accuracy of news (Pennycook and Rand 2019 ). The accuracy of the news is completely based on the wisdom of the crowd (Ahmed et al. 2019 ). Kiskkit is an example of a platform that can be used for crowdsourcing where the platform allows a group of people to evaluate pieces of a news article (Hassan et al. 2017 ). After one piece has been evaluated the crowd moves to the next piece for evaluation until the entire news article has been evaluated and the accuracy thereof has been determined by the wisdom of the crowd (Hassan et al. 2017 ).

Hybrid Approach

There are three generally agreed upon elements of fake news articles, the first element is the text of an article, second element is the response that the articles received and lastly the source used that motivate the news article (Ruchansky et al. 2017 ). A recent study has been conducted that proposes a hybrid model which helps to identify fake news on social media through using a combination of human and machine learning to help identify fake news (Okoro et al. 2018 ). Humans only have a 4% chance of identifying fake news if they take a guess and can only identify fake news 54% of the time (Okoro et al. 2018 ). The hybrid model as proven to increase this percentage (Okoro et al. 2018 ). To make the hybrid model effective it combines social media news with machine learning and a network approach (Okoro et al. 2018 ). The purpose of this model is to identify the probability that the news could be fake (Okoro et al. 2018 ). Another hybrid model called CSI (capture, score, integrate) has been developed and functions on the main elements; (1) capture - the process of extracting representations of articles by using a Recurrent Neutral Network (RNN), (2) Score – to create a score and representation vector, (3) Integrate – to integrate the outputs of the capture and score resulting in a vector which is used for classification (Ruchansky et al. 2017 ).

In this paper we discussed the prevalence of fake news and how technology has changed over the last years enabling us to develop tools that can be used in the fight against fake news. We also explored the importance of identifying fake news, the influence that misinformation can have on the public’s decision making and which approaches exist to combat fake news. The current battle against fake news on COVID-19 and the uncertainty surrounding it, shows that a hybrid approach towards fake news detection is needed. Human wisdom as well as digital tools need to be harnessed in this process. Hopefully some of these measures will stay in place and that digital media platform owners and public will take responsibility and work together in detecting and combatting fake news.

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Lesson Plan

Dec. 13, 2016, 3:29 p.m.

How to teach your students about fake news

Fake news is making news, and it’s a problem.

Not only did a BuzzFeed data analysis find that viral stories falsely claiming that the Pope endorsed Donald Trump and that Hillary Clinton sold weapons to terrorists receive more Facebook attention than the most popular news stories from established news outlets, but a false story about child trafficking in a Washington, D.C. pizza restaurant inspired a North Carolina man to drive 5 hours with a shotgun and other weapons to investigate.

This lesson gives students media literacy skills they need to navigate the media, including how to spot fake news.

Social studies, U.S. government, civics, journalism

Estimated Time

One 50-minute class

Grade Level

Introduction.

A recent study by Stanford University found an overwhelming majority of students were not able to tell the difference between so-called fake news and real news. Part of the solution involves providing students with the media literacy skills they need to evaluate sources, including social media. With the help of NewsHour Extra, students will explore the problems with fake news and gain confidence exploring the media that they come across every day.

Essential question

How do you know if a news source is reliable?

Warm up activity

Complete the following PBS NewsHour Extra Daily News Story activity with your students: Did fake news influence the outcome of Election 2016? You may also want to show your class Craig Silverman’s story from Buzzfeed, which is the subject of the NewsHour piece.

Note: Given time constraints, you may choose to watch the video, read the text or choose which questions you will address with your students.

Main activity

• Share the following with your students: Prof. Wineburg says one mistake schools make is to block certain websites from students while they are at school. “In many schools there are internet filters that direct students to previously vetted sites and reliable sources of information. But what happens when they leave school and they take out their phone and they look at their Twitter feed? How do they become prepared to make the choices about what to believe, what to forward, what to post to their friends when they’ve given no practice in doing those kinds of things in school?” Do your students agree with Dr. Wineburg? Should schools block certain websites? Why or why not?
• The News Literacy Project and Checkology created a checklist of “Ten questions for fake news detection.” Read it out loud with your students. Ask them if they have any questions about the checklist and which points they think will help them the most when it comes to detecting fake news.

• Nope Francis: Reports that His Holiness has endorsed Republican presidential candidate Donald Trump originated with a fake news web site
• How might students be able to figure out that these are fake news stories? What points from News Literacy Project’s checklist apply to these pieces? What should you do if you still have questions about the legitimacy of a source?

*Note : If you have any questions or concerns, talk with your technology coordinator or administrator and perhaps think about sending an email home letting parents know you are teaching important media literacy skills. There are many wonderful resources to help teachers learn about media literacy. Here are just a few to check out: The News Literacy Project , NAMLE , Media Education Lab , Project Look Smart and Center for News Literacy .

Extension Activities

• Fake news might be a case of history repeating itself. Check out the role fake news has played in U.S. history in this Washington Post piece: Fake News? That’s a very old story.
• Who are some of the people behind fake news? What would make a person want to create a fake news story? This Eastern European teenager says he’s just giving people what they want and making a lot more than the average yearly income of $5,000 in his hometown. Take a look at this NBC News story: Fake news: How a partying Macedonian teen earns thousands publishing lies
• We also recommend Full Frontal with Samantha Bee’s piece on ‘Fake News, Real Consequences’ but for more mature high school students. Be sure to preview before you show your class.

By Victoria Pasquantonio, PBS NewsHour education editor and 13-year social studies and English teacher.

Fill out this form to share your thoughts on Classroom’s resources. Sign up for NewsHour Classroom’s ready-to-go Daily News Lessons delivered to your inbox each morning.

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Real news/fake news: detecting fake news.

• About Fake News
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Deepfake Videos

Identifying and Tackling Manipulated Media . (Reuters and Facebook)

Is that video real?  spotting deepfake videos (CNN 10/19/2020)

Deepfakes Are Going to Wreak Havoc on Society.  We are not prepared . (Forbes 5/26/2020)

In the Age of AI, is Seeing Still Believing?   (New Yorker 11/5/2018)

How to Recognize Fake News

How To Recognize A Fake News Story , Huffington Post, November 22, 2016

And more tips:.

Breaking News Consumer's Handbook:  Fake News Edition   On the Media November 18, 2016

Evaluating Sources/Distinguishing Popular from Scholarly Sources

UCB Library  guide to Evaluating Sources , includes tips for looking at any potential information source:

• Authority  - Who is the author? What is their point of view? 
• Purpose  - Why was the source created?  Who is the intended audience?
• Publication & format   - Where was it published? In what medium?
• Relevance  - How is it relevant to your research? What is its scope?
• Date of publication  - When was it written? Has it been updated?
• Documentation  - Did they cite their sources? Who did they cite?

See the guide for more, including distinguishing popular versus scholarly sources.

Let's Check a Claim

Tools for Learning More

• Tools for Bursting Your Filter Bubble Tools to help you read beyond the narrow perspective of your pre-existing political perspective.
• AlllSides Bias Ratings The AllSides link lists its ratings of various news media/sites: Left, Center, Right and displays users' ratings of the same sites.
• WhoIs WhoIs allows you to look up the owner of an Internet domain.
• Hoaxy (beta): Hoaxy visualizes how fake news stories and fact-checking stories spread on social media. Hoaxy is a project of the Indiana University Network Science Institute and the Center for Complex Networks and Systems Research.
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• Last Updated: Aug 15, 2024 9:39 AM
• URL: https://guides.lib.berkeley.edu/fake-news

• Open access
• Published: 26 April 2023

Fake news detection on social media: the predictive role of university students’ critical thinking dispositions and new media literacy

• Ali Orhan   ORCID: orcid.org/0000-0003-1234-3919 1  

Smart Learning Environments volume  10 , Article number:  29 ( 2023 ) Cite this article

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This study aimed to investigate the predictive role of critical thinking dispositions and new media literacies on the ability to detect fake news on social media. The sample group of the study consisted of 157 university students. Sosu Critical Thinking Dispositions Scale, New Media Literacy Scale, and fake news detection task were employed to gather the data. It was found that university students possess high critical thinking dispositions and new media literacies as well as high fake news detection abilities and there is a positive and moderate relationship among these variables. Also, this study revealed that critical thinking dispositions and new media literacies significantly predicted university students’ abilities to detect fake news on social media and they together explained 18% of the total variance on fake news detection. Besides, university students’ critical thinking dispositions presented a larger effect on their abilities to detect fake news than new media literacies.

Introduction

With the great enhancement of the internet, social media (SM) has become one of the most widely used sources of information today and a great number of people use SM platforms to learn news (Aldwairi & Alwahedi, 2018 ). There is a great pile of information on the internet and this can be disseminated very easily and quickly on SM. We can learn breaking news quicker on SM than any other conventional means of communication. However, there is a great problem here. Although SM provides a space for news to spread at an impressive rate, it can also become a hotbed of misinformation (Gaozhao, 2021 ; Shu et al., 2017 ). Instead of reaching true and unbiased news, people are bombarded with a great number of fake news (FN) on SM. As a recent example, there has been a rash of digital FN about COVID-19 on SM resulting in undesired health, social, and cultural consequences (Kouzy et al., 2020 ). Also, FN had an undeniable role in the Brexit Referendum and in the USA Presidential Election in 2016 (Allcott & Gentzkow, 2017 ; Bastos & Mercea, 2019 ).

FN can be briefly defined as inaccurate or fictitious content which is released or disseminated as real information although it is not (Gaozhao, 2021 ). FN—in other words, fabricated news—can be easily disseminated in the form of real news either on SM or through other conventional means of communication (Molina et al., 2021 ) and it does not have any objective evidence to show the authenticity of the information which it conveys (Pennycook & Rand, 2021 ). FN attracts a lot more attention and spreads more quickly than real news and it mostly includes emotionally charged language (Vosoughi et al., 2018 ). Although FN phenomenon has hundreds of years of history (Tandoc et al., 2018 ) and the ability to detect it has been prized for a long time (Beiler & Kiesler, 2018 ; Burkhardt, 2017 ), its reach and mostly deleterious effect have elevated significantly today because of a wide range of SM platforms (Allcott & Gentzkow, 2017 ). FN is undesirable because it can influence people in a psychological and social way by distorting their beliefs resulting in misinformed and wrong decisions (Zimmermann & Kohring, 2020 ). Also, FN does not only negatively affect individuals, but it is also harmful to society in many ways. It can have many political, economic, social, and cultural consequences. People can lose their trust in the media (Vaccari & Chadwick, 2020 ) and balance of the news ecosystem can be ruined because of the increasing spread of FN (Shu et al., 2017 ).

Therefore, although it is vital to ascertain news veracity on SM where information is easily accessible, it is not easy to do this because of the nature of SM (Hernon, 1995 ). SM users can have broad access to produce information without any filtering or editorial judgement (Allcott & Gentzkow, 2017 ) and most of the content on SM is spontaneous and unprofessional (Robinson & DeShano, 2011 ). Besides, the great volume of information on SM makes it impossible to check the authenticity of the news on SM (Pennycook & Rand, 2019 ; Zhang & Ghorbani, 2020 ). Also, people tend to share the information easily and repeatedly with others when they believe FN is true which proliferates the spread of FN (Oh et al., 2018 ) and they can unwittingly contribute to the dissemination of FN produced by others on SM.

Although producing and consuming FN are clearly two different behaviors, the difference between them has become blurred because of the characteristics of the SM platforms. Individuals can easily create, share, and consume information on SM within a few seconds from anywhere and at any time. Therefore, individuals can easily change their roles from FN producers to consumers, or vice versa (Kim et al., 2021 ). Also, they can do this intentionally or unintentionally. Therefore, SM is a great place for FN to become extremely influential and spread extremely fast. As SM provides a space for the proliferation of FN and a lot of people rely on SM platforms as the main source of information (Lazer et al., 2018 ), educating people to fight against FN and equipping them with the necessary tools to identify FN are crucial (Zhang & Ghorbani, 2020 ). Previous literature indicates that critical thinking (CT) and new media literacy (NML) are two of the most essential tools that can be used by individuals to protect themselves against FN on SM.

Critical thinking and fake news detection

CT is a functional, reflective, and logical thinking process employed by individuals before deciding what to do or what to believe (Ennis, 2000 ). In other words, CT can help individuals to make true and reasonable decisions about their actions or on the accuracy of information. CT leads to more accurate and systematic processing of ideas, arguments, and information (Ruggerio, 1988 ) in which the quality and accuracy of them are examined and evaluated (Lewis & Smith, 1993 ) and after this careful and logical examination process, they decide to believe or support them. Therefore, it can be said that CT works as armor that protects individuals against fake information (Epstein & Kernberger, 2012 ). An adequate critical thinker is “habitually inquisitive, well-informed, trustful of reason, open-minded, fair-minded in evaluation, prudent in making judgments, willing to reconsider, diligent in seeking relevant information, reasonable in the selection of criteria, and focused in inquiry” (Facione, 1990 , p. 2). For adequate critical thinkers, all assumptions are questionable and divergent ideas are always welcomed. They are always willing to inquire and this inquiry is not affected by their emotions, heuristics, or prejudices, and is not biased in favor of a particular outcome (Kurfiss, 1988 ).

Therefore, CT can be seen as an effective weapon to combat FN (Bronstein et al., 2019 ; Wilson, 2018 ). CT is a thinking process employed by individuals to perceive whether the information is real or fake (Paul, 1990 ). Good critical thinkers—in other words, people who have high CT skills and dispositions at the same time—tend to examine and evaluate the news they encounter on SM to see if it is accurate and real (Lewis & Smith, 1993 ). They evaluate the sensibility and accuracy of given news, examine the source of it, and look for sound evidence to trust in the accuracy of it (Mason, 2008 ). Based on the results of this careful examination process, they can decide whether to share this news with others or not. Therefore, CT can also be seen as an important barrier against the proliferation of FN on SM. While individuals lacking CT tend to share the information easily and repeatedly with others without checking the accuracy of it resulting in the quick spread of FN (Oh et al., 2018 ), individuals with high CT skills and dispositions tend to evaluate the content and the source of it before sharing. After this careful examination based on sound evidence instead of heuristics and emotions (Kahneman, 2011 ), they can decide not to share it with others if they decide that it is fake and misleading or it does not have strong arguments, and hence, they can break the chain and decelerate the dissemination of FN on SM. Therefore, it can be said that adequate critical thinkers not only do not fall into traps of FN but also they do not contribute to the dissemination of FN produced by others on SM.

Previous literature has reported some empirical evidence indicating CT has a positive effect on detecting FN on SM. In their study with 1129 participants, Escola-Gascon et al. ( 2021 ) found out that CT dispositions significantly predicted the detection of FN. In their experimental study aiming to examine if adding CT recommendations to SM posts can help people to better discriminate true news from FN, Kruijt et al. ( 2022 ) concluded that participants who were exposed to CT recommendations presented better performance to detect FN. Lutzke et al. ( 2019 ) found that participants exposed to guidelines priming CT performed better to detect FN in their experimental study which was carried out to investigate the effectiveness of guidelines priming CT on willingness to trust, like, and share FN.

New media literacy and fake news detection

NML can be seen as a broader term which involves different kinds of literacy like classic (e.g., reading and writing), audiovisual (electronic media), digital, and information literacies. It includes some important process skills like access, analysis, evaluation, critique, production, and participation in media content (Hobbs & Jensen, 2009 ; Lee et al., 2015 ; Zhang et al., 2014 ). The interaction between individuals and media content can be divided into two categories, namely, consuming and prosuming (Toffler, 1981 ). Also, the term of literacy can be divided into two categories which are functional literacies and critical literacies (Buckingham, 2003 ). While functional literacies, which are related to skills and knowledge, refer to individuals’ capability of knowing how, critical literacies are about individuals’ capability of meaning-making and evaluating the credibility, accuracy, and usefulness of the message (Buckingham, 2003 ). Based on these two categorizations, a conceptual framework for NML is proposed by Chen et al. ( 2011 ). This framework proposes that NML includes functional consuming, functional prosuming, critical consuming, and critical prosuming literacies. While functional consuming literacy is about individuals’ capability of gaining access to created new media content and understanding the message it conveys, critical consuming literacy refers to individuals’ capability of investigating the media content in terms of different perspectives such as cultural, political, social, and economic (Chen et al., 2011 ). Also, while functional prosuming literacy refers to the ability to create media content, critical prosuming literacy involves the contextual interpretation of the media content by individuals during their activities on media (Chen et al., 2011 ).

Individuals with high NML are aware of the way the messages are created, disseminated, and commercialized all over the world (Thoman & Jolls, 2004 ) and can use different media platforms consciously, distinguish and evaluate different media content, investigate the media types, its effects, and the messages they convey in a critical way, and (re)produce new media content (Kellner & Share, 2007 ). In other words, new media literate individuals can critically access, decode, understand, and analyze the messages which various kinds of media content convey (Leaning, 2017 ; Potter, 2010 ) and they can make independent judgements about the veracity of media content (Buckingham, 2015 ; Leaning, 2017 ). Therefore, we can say that high NML provides the necessary skills for individuals to actively investigate, evaluate, and analyze the media content and its underlying messages instead of passively consuming the media content and accepting the veracity of the conveying messages which can include potentially misinformation and disinformation without thinking, and hence, it can protect individuals from the negative effects of new media platforms (Hobbs, 2017 ). New media literate individuals are capable of investigating and evaluating the credibility of the information or news in a critical way, verifying the authenticity of them, and using them ethically. In short, NML increases the possibility that individuals take a critical standpoint toward FN (Kim et al., 2021 ) and it has an important effect on the degree of consumption and dissemination of FN on SM (Staksrud et al., 2013 ). Therefore, new media literate individuals can not only protect themselves against the consuming FN but also they are possibly going to be unwilling to share the news without being sure about the accuracy of it, and hence, they are going to have a proactive role in stopping the spread of FN (Parikh & Atrey, 2018 ).

Previous research has provided some empirical evidence indicating NML has a positive effect on the ability to detect FN on SM. In their study aiming to investigate the relation between students’ level of new media literacies and their ability to discern FN, Luo et al. ( 2022 ) concluded that NML and FN detection performance are significantly related to each other. In her experimental study aiming to investigate the effectiveness of media and information literacy on FN detection, Adjin-Tettey ( 2022 ) found that media and information literacy trained participants were more likely to detect FN and less likely to share it. Similarly, Al Zou’bi ( 2022 ) carried out an experimental study to investigate the effectiveness of media and information literacy on FN detection and concluded that media and information literate students presented better abilities to detect FN. Moore and Hancock ( 2022 ) also reported similar results in their experimental study. Besides, Guess et al. ( 2020 ) concluded that participants who received media literacy education were more successful in FN detection. Also, Lee et al. ( 2022 ) concluded that NML possesses an important role on mitigating the FN problem in their study which was carried out to investigate the effectiveness of NML on perception of FN, media trust, and fact-checking motivation.

The current study

FN is an important problem and it can pollute the public sphere and harm democracy, journalism, and freedom of expression (Pogue, 2017 ). Indeed, it is listed as one of the most important threats to society by the World Economic Forum (Del Vicario et al., 2016 ). Therefore, equipping individuals with the necessary tools to identify FN is crucial (Zhang & Ghorbani, 2020 ). However, the usage of these necessary tools, especially cognitive ones, to combat FN is not investigated sufficiently (Machete & Turpin, 2020 ; Wu et al., 2022 ) and there is a clear need for other studies investigating what can be done and how these tools can be used to combat against FN (Au et al., 2021 ). Previous literature has showed that CT and NML, which can also be seen as a survival kit for this century, are two of the most essential cognitive tools that can be used by individuals to protect themselves against FN on SM. Although there is a well-established theoretical base regarding the positive effect of CT and NML on FN detection, there is not enough empirical evidence indicating the positive role of CT and NML in fighting against FN in the literature (Xiao et al., 2021 ; Zanuddin & Shin, 2020 ). Also, most of the previous research regarding the positive effect of CT and NML on FN detection consists of correlational and experimental studies and there are not enough studies examining the predictive role of CT and NML on FN detection, and hence, empirical evidence regarding the predictive power of these variables is limited. Therefore, we can say that examining the predictive power of CT and NML on FN detection is a promising area of research that may be useful to shed light on what extent these two variables are effective on FN detection on SM. The dearth of research on the predictive role of CT and NML on FN detection provides a sufficient reason for this study aiming to examine the effectiveness of CT and NML on FN detection. Therefore, this study aimed to examine the predictive role of CT dispositions and NML of university students on their ability to detect FN on SM. To this end, the following questions were sought:

What are university students’ levels of CT dispositions and NML?

Are university students’ Sosu Critical Thinking Dispositions Scale (CTDS) and New Media Literacy Scale (NMLS) scores significant predictors of their ability to detect FN?

In this non-experimental quantitative study, a cross-sectional survey design was used. University students’ ability to detect FN was determined as the dependent variable of the study and their scores on the CTDS and NMLS were determined as predictor variables.

Study group

This study was conducted with 157 university students (66 females, 91 males) studying in a state university in Turkey in the academic year of 2022–2023. The students were recruited on a voluntary basis. The mean age of them was 18.96 (SD = 1.00) and their age ranged between 17 and 24. All of the students were in their one-year English preparatory year which is compulsory for them before starting their education in their departments. They are learning only English in this year. The majority of students’ mothers graduated from high school (31.8%) and primary school (29.3%) while most of their fathers are high school (38.2%) and university (25.5%) graduates. A-priori power analysis was conducted using G*Power 3 by Faul et al. ( 2007 ) for linear multiple regression analysis (alpha = 0.05; power = 0.95; two predictors) and it showed that the minimal sample size should be 107 to detect a medium effect size (f 2  = 0.15). So, it can be said that the sample size of 157 was adequate.

Data collection tools

Sosu critical thinking dispositions scale (ctds).

The CTDS developed by Sosu ( 2013 ) and adapted into Turkish by Orhan ( 2023 ) was used to measure the university students’ CT dispositions. The CTDS has 11 items and two sub-dimensions, namely, critical openness (7 items) and reflective skepticism (4 items). Turkish adaptation study with two independent samples indicated that the Turkish version of CTDS has the same factor structure as the original one. The reliability coefficient of the CTDS was found to be 0.92 for sample 1 and 0.94 for sample 2 in the adaptation study. In this study, the reliability coefficient was calculated as 0.80 for the total scale.

New Media Literacy Scale (NMLS)

The NMLS developed by Koç and Barut ( 2016 ) was used to determine students’ new media literacies. The NMLS has 35 items and four sub-dimensions, namely, functional consumption (7 items), critical consumption (11 items), functional prosumption (7 items), and critical prosumption (10 items). The reliability coefficients of the sub-dimensions ranged between 0.85 and 0.93 while it was calculated as 0.95 for the total scale. In this study, the reliability coefficient was 0.92 for the total scale.

• Ability to detect fake news

The university students’ ability to detect FN on SM was measured using a FN detection task created by the researcher based on the previous study of Preston et al. ( 2021 ). The FN detection task includes six news items, three of them present real news content while three of them include FN content. The FN items include topics related to a claim that Red Cross has ceased its activities in Ukraine (fake), a claim that NASA has stopped its research on oceans (fake), and a claim that Starbucks no more accepts payment by cash money (fake). The first real news item claims that Dwayne Johnson has become the highest-paid actor for the second consecutive year. The second real news item says that an electric bus produced by KARSAN (a Turkish company) started to provide public transportation services in Norway. The third real news indicates that Turkey has become the country that produces the most figs in 2020 with 320 thousand tons of production according to 2020 data from the Food and Agriculture Organization of the United Nations. Two independent and impartial fact-checking websites ( www.dogrula.org and www.teyit.org ) were used to obtain information related to fake and real news items. These two websites are really popular in Turkey and free to use as a fact-checking resource.

Four main components, namely, news sharing source, original news item source, content level, and author argument were considered while developing the mock Facebook post items to increase the possibility for the students to evaluate levels of objectivity, professionalism, argument strength, and trustworthiness in the items. The news items look like a typical Facebook news post including likes, comments, and shares in which an article is shared by an organization related to its content (see Fig.  1 ). For example, the Facebook page named “Sinema & Sinema” is sharing an article from “boxofficeturkiye.com”. The number of comments, likes, and shares are similar between fake and real news items groups in order not to affect students’ choices.

Examples of FN items (on the left) and real news items (on the right)

For the first component, more objective content names (e.g. Sinema & Sinema) were used for the real news to influence impressions of professionalism and objectivity while more subjective-sounding content names (e.g. bilim günlüğü) were chosen for FN. Also, for the second component, more objective content suggesting website names were chosen for the real news items (e.g. boxofficeturkiye.com) while FN items included websites suggestive of more subjective content (e.g. savunmatr.com). For the third component, FN items presented short information written in a subjective style and without using any credible source to suggest low trustworthiness. For the fourth component, FN items included author arguments written using emotive language and without references to reliable sources suggesting subjectivity and low levels of argument strength, professionalism, and trustworthiness. On the other hand, I employed the opposite strategies like references to reliable sources and non-emotive language for the real news items.

After preparing the six news items, students were asked to critically analyze each of them and answer four questions prefaced with the text “to what extent do you agree with the following statement”. The first question is “the author and shared article are objective” and it aims to evaluate the level of objectivity. The second question which aims to evaluate professionalism is “the article seems to be produced by a professional”. The third question designed to evaluate the argument strength is “the article presents a strong argument”. The last question is “this source of information is credible and trustworthy” and it aims to evaluate the trustworthiness. Students can answer the questions via 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Students’ responses to the FN are reverse-coded. Scores on the FN detection task can range from 24 to 120 with a midpoint of 60. Higher scores indicate a stronger ability to detect FN while lower scores indicate a weaker ability to detect FN. The reliability coefficient calculated for the FN detection task was 0.84 in this study.

Data collection

Following the ethical committee approval from ZBEU, the data were gathered in the fall term of 2021–2022 academic year. Privacy and confidentiality issues and the aim of the study were shared with all students and they were informed about their right to withdraw from the study if they want. Students completed the instruments in about 30 min.

Data analysis

First, all variables were investigated to see whether they had any missing data and it was seen that they had no missing data. After that, the data were investigated in terms of normality and Skewness and Kurtosis values indicated that the data presented normal distribution for each variable (see Table 1 ). Then, possible multivariate outliers and outliers per variable were checked with Mahalanobis Distance scores and Z transformation values. These scores revealed that the data did not have any influential outliers which should be excluded. Pearson correlation, CI, VIF, and tolerance values were examined to check if there is a high correlation among the variables and it was seen that there is no high correlation. Descriptive statistics, Pearson correlation, and multiple linear regression with enter method were conducted using SPSS 20 statistical software to analyze the data.

As shown in Table 1 , university students presented high CT dispositions ( \(\overline{{\text{X}}}\)  = 3.89) and new media literacies ( \(\overline{{\text{X}}}\)  = 3.83). Also, their mean score for the FN detection task is 76.84. As the scores of the FN detection task can range between 24 and 120 with a midpoint of 60, we can say that the students have higher scores than the midpoint indicating that they have high abilities to detect FN.

As shown in Table 2 , university students’ CT dispositions ( r  = 0.399) and new media literacies ( r  = 0.303) have a moderate and positive relationship with their abilities to detect FN. Also, there is a positive and moderate relationship between university students’ CT dispositions and new media literacies ( r  = 0.417).

As it can be seen in Table 3 , multiple linear regression analysis results indicated that university students’ CT dispositions (β = 0.329, t (157)= 4.106, p < 0.05) and new media literacies (β = 0.165, t (157)= 2.062, p < 0.05) significantly predicted their abilities to detect FN on SM (R = 0.426, R 2  = 0.181, p < 0.01). One-way ANOVA test results showed that the established regression model was significant (F (2,156)  = 17.065, p < 0.01). Students’ CT dispositions and new media literacies together explained 18% of the total variance on their abilities to detect FN on SM. Besides, university students’ CT dispositions (β = 0.329) presented a larger effect on their abilities to detect FN than new media literacies (β = 0.165).

This study aimed to investigate the predictive role of CT dispositions and NML of university students on their ability to detect FN on SM. It was seen that university students presented high CT dispositions and NML as well as high abilities to detect FN. Another result obtained in the study revealed that CT dispositions and NML of university students were positively and moderately related to their abilities to detect FN. Also, a positive and moderate relationship between university students’ CT dispositions and NML was found.

This study also revealed that university students’ CT dispositions and new media literacies significantly predicted their abilities to detect FN on SM. Students’ CT dispositions and new media literacies together explained 18% of the total variance on their abilities to detect FN on SM. Previous literature revealed similar results regarding the positive effect of CT dispositions (Escola-Gascon et al., 2021 ; Kruijt et al., 2022 ; Lutzke et al., 2019 ) and NML (Adjin-Tettey, 2022 ; Al Zou’bi, 2022 ; Guess et al., 2020 ; Lee et al., 2022 ; Luo et al., 2022 ; Moore & Hancock, 2022 ) on FN detection on SM. Therefore, we can say that previous literature confirmed the results of this study.

Individuals with high CT skills and dispositions do not make instant decisions about their behaviors or the accuracy of information without employing a systematic and logical thinking process in which they examine and evaluate the quality and accuracy of ideas, arguments, and information (Lewis & Smith, 1993 ; Ruggerio, 1988 ). They acquire the most accurate information about their environment and can make the best decision about their actions thanks to CT. Individuals wear CT as armor and protect themselves against fake information (Epstein & Kernberger, 2012 ). Therefore, we can say that CT is a vital skill for individuals in their daily life and it is even more important during the time they spend on SM where people have been bombarded with a great number of FN. An adequate critical thinker tends to examine and evaluate the accuracy of the news they encounter on SM (Lewis & Smith, 1993 ) and they are unwilling to share this news with others until they make sure about the sensibility and accuracy of it (Mason, 2008 ). Therefore, CT not only helps individuals not to fall into traps of FN but also works as a barrier against the dissemination of FN produced by others on SM which makes CT an important and effective weapon to combat FN (Bronstein et al., 2019 ; Wilson, 2018 ). Indeed, Machete and Turpin ( 2020 ) concluded that previous relevant literature indicated that CT is an important skill to identify FN in their systematic review study aiming to present the current state of the literature on the usage of CT to detect FN.

Individuals with high NML possess the ability to access, decode, understand, and analyze the messages which different kinds of media content convey (Leaning, 2017 ; Potter, 2010 ). They can also make independent judgements about the veracity of these media content (Buckingham, 2015 ; Leaning, 2017 ). New media literate individuals tend to employ an active process in which they investigate, evaluate, and analyze the media content and its underlying messages instead of passively consuming it because they are aware of the way the media content is created, commercialized, and disseminated all over the world (Thoman & Jolls, 2004 ) and they know that they can include potentially misinformation and disinformation. Therefore, NML equips individuals with the necessary skills to investigate and evaluate the credibility of the information or news and its source, verify the authenticity of them, and use them ethically. Thanks to NML, individuals take a critical standpoint toward FN (Kim et al., 2021 ) and it decreases the possibility of consumption and dissemination of FN on SM (Staksrud et al., 2013 ).

Therefore, we can say that the results of this study indicating CT dispositions and NML were significant predictors of university students’ abilities to detect FN coincide with the theoretical background and the results of previous research. Besides, university students’ CT dispositions presented a larger effect on their abilities to detect FN than new media literacies. This finding shows that CT dispositions are a much more powerful weapon in fighting against FN than NML. CT dispositions equip individuals with the necessary skills to examine and evaluate the quality and accuracy of ideas, claims, and judgments as well as of their source. Also, individuals with high CT dispositions are open to new ideas and they are willing to modify their ideas and arguments when a piece of convincing evidence appears. Therefore, individuals with high CT dispositions are skeptical of any information they encounter in their daily life and they habitually tend to evaluate the veracity of information itself as well as the source of this information. On the other hand, NML is not only about consuming but also producing media content. New media literate individuals are capable of consuming media content in a critical way. They have the necessary skills to actively evaluate the credibility, accuracy, and usefulness of the media content and they can investigate it in terms of different perspectives such as cultural, political, social, and economic. Also, NML is about the capability of gaining access to created new media content, understanding the message it conveys, and (re)producing new media content. They can also use different media platforms consciously, distinguish different media content, and investigate the media types. Correspondingly, we can say that consuming literacies are directly related to FN detection while producing skills have an indirect relation with FN detection ability. On the other hand, CT dispositions are directly related to abilities to detect FN. Therefore, we can say that the result regarding CT dispositions are more effective to combat FN can be explained by this.

In short, this study showed that CT dispositions and NML were significant predictors of university students’ abilities to detect FN. This result which is confirmed by previous research shows the important role of CT dispositions and NML to combat FN which is one of the most important threats to society in today’s world (Del Vicario et al., 2016 ) and can damage democracy, journalism, and freedom of expression (Pogue, 2017 ). We can say that individuals possessing high CT dispositions and NML are more competent to combat FN. They can not only protect themselves against FN on SM but also do not contribute to the dissemination of FN by preferring not to share them with other people. Consequently, CT dispositions and NML should be implemented during the effort of fighting against FN on SM. CT dispositions (Kennedy et al., 1991 ; Lewis & Smith, 1993 ) and NML (Buckingham, 2003 ; Hobbs & Jensen, 2009 ) are teachable skills through appropriate education, and hence, enhancing individuals’ CT dispositions and NML should be included among the most important aims of educational systems because they do not only positively contribute to individuals’ daily and school life (Halpern, 2003 ; Orhan, 2022a , 2022b ; Paul & Elder, 2001 ) but also work as important barriers against the consumption and dissemination of FN on SM. Therefore, we can say that enhancing individuals’ CT dispositions and NML would be a good idea to equip them with the necessary skills that are useful in the fight against FN on SM. It can be said that this study has significantly contributed to the literature by presenting additional evidence regarding the predictive role of CT dispositions and NML on the ability to detect FN on SM because previous literature lacks enough evidence indicating the predictive roles of these two variables (Xiao et al., 2021 ; Zanuddin & Shin, 2020 ). Also, this study differs from the previous studies because it investigated the predictive role of CT dispositions and NML on FN detection comparatively and showed that CT dispositions are a more powerful weapon in fighting against FN than NML.

Limitations and implications for further research

This study has several limitations although it provides important results regarding the predictive role of CT dispositions and NML on FN detection. First, the study group can be shown as a limitation because this study was conducted with a study group consisting of only university students. Therefore, similar studies can be conducted with other sample groups consisting of students from various educational levels, especially high school because high school students spend most of their time on SM. Second, data collection tools can be shown as another limitation of the study because only self-report quantitative tools were employed to gather the data for this study and these tools can be influenced by social desirability. Therefore, further studies using qualitative or mixed methods can be conducted to better understand the predictive role of CT dispositions and NML on FN detection. Third, several other factors like students’ level of media knowledge may affect their ability to detect FN on SM. However, these factors were not taken into account in this study and this can be shown as the third limitation of the study.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Critical thinking

• New media literacy
• Social media

Critical Thinking Dispositions Scale

New Media Literacy Scale

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Orhan, A. Fake news detection on social media: the predictive role of university students’ critical thinking dispositions and new media literacy. Smart Learn. Environ. 10 , 29 (2023). https://doi.org/10.1186/s40561-023-00248-8

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Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

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1 Introduction

The increased scholarly focus has been directed to fake news detection given their widespread impact on supply chain disruptions, as was the case with the COVID-19 vaccine. Fake news and misinformation are highly disruptive, which create uncertainty and disruptions not only in society but also in business operations. Fake news and disinformation-related problems are exacerbated due to the rise of social media sites. Regarding this, using artificial intelligence (AI) to counteract the spread of false information is vital in acting against disruptive effects (Gupta et al., 2021 ). It has been observed that fake news and disinformation (FNaD) harm supply chains and make their operation unsustainable (Churchill, 2018 ). According to research, fake news can be classified into two distinct concepts of misinformation and disinformation (Petratos, 2021 ; Allcott & Gentzkow, 2017 ) defined fake news as “ news articles that are intentionally and verifiably false, and could mislead readers ” (p. 213). According to Wardle ( 2017 ), misinformation refers to “ the inadvertent sharing of false information ”, while disinformation can be defined as “ the deliberate creation and sharing of information known to be false ”. Among the negative consequences that fake news can have for companies are loss of sponsorships, reduced credibility, and loss of reputation which can adversely affect performance (Di Domenico et al., 2021 ). In such a context AI is shaping decision-making in an increasing range of sectors and could be used to improve the effectiveness of fake news timely detection and identification (Gupta et al., 2021 ). Whereas many new efforts to develop AI-based fake news detection systems have concentrated on the political process, the consequences of FNaD on supply chain operations have been relatively underexplored (Gupta et al., 2021 ).

Kaplan and Haenlein ( 2019 ) addressed AI “ as a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation ” (p.17). Although emerging technologies such as AI may sometimes have negative effects, they can be utilized to combat disinformation. As scholarship is showing increasing interest in how AI can improve operationally and supply chain efficiencies (Brock & von Wangenheim, 2019 ), researchers have recently called for more studies on how organizational strengths and the use of AI influence the outcomes for decision-making structures (Shrestha et al., 2019 ). Fake news has considerable negative effects on firms’ operations, such as repeated disruptions of supply chains (Churchill, 2018 ). FNaD influence the use of a company’s product or services (Zhang et al., 2019 ; Sohrabpour et al., 2021 ) argued that leveraging AI to improve supply chain operations will likely improve firms’ planning, strategy, marketing, logistics, warehousing, and resource management in the presence of any environmental uncertainty, including that caused by FNaD.

Scholars have called for research to attain an in-depth understanding of AI and of how to tailor it to enhance business efficiencies and minimize supply chain disruptions (SCDs) (e.g., Grewal et al., 2021 ; Churchill, 2018 ). The extant literature has drawn mixed conclusions on whether AI-driven or hybrid AI decision-making benefits a firm’s supply chain (Shrestha et al., 2019 ). The question of why some firms are more effective than others in using AI to manage SCDs has largely been overlooked (Toorajipour et al., 2021 ). Increased research efforts are being made to identify and manage fake news risk in supply chain operations (Reisach, 2021 ). In today’s digital media landscape, the term ‘fake news’ has gained relevance following the 2016 US presidential elections (Allcott & Gentzkow, 2017 ). People have been observed to be unable to clearly distinguish between fake and real news and to tend to perceive ‘fake news’ as a more significant issue within the current information landscape (Tong et al., 2020 ). Therefore, decision-makers are often influenced by FNaD, thus ending up making erroneous decisions and drawing inaccurate conclusions regarding current scenarios (e.g., Lewandowsky et al., 2012 ; Di Domenico et al., 2021 ). From a supply chain perspective, researchers have highlighted how FNaD can lead to SCDs (e.g., Gupta et al., 2021 ; Kovács & Sigala, 2021 ; Sodhi & Tang, 2021 ), which can have a far-reaching impact on the functioning of global supply chains.

Additionally, the United Nations ( 2020 ) have suggested that, despite the measures put in place to build confidence in people, businesses, and supply chain operations, SCDs have remained a problematic area for businesses in recent years. Resilinc ( 2021 ) revealed that SCDs have been increasing by 67% year-over-year, with 83% of such disruptive events being caused by human activity—not natural disasters. EverStream Analytics (2020) found that 40.5% and 33.4% of businesses are respectively getting their information and intelligence relating to supply chain issues from their customers and social media. The detection of fraudulent information is thus critical to avoid such consequences (Kim & Ko, 2021 ), and businesses need to set up specific processes or routines to filter incoming business-related information and mitigate any possible related harm to their operations (Kim & Ko, 2021 ; Kim & Dennis, 2019 ) emphasized research underpinning emerging technologies such as AI suited to tackle FNaD. As FNaD have become increasingly relevant in the field of operations management, and given their effects on decision-making, there is a need to understand what business processes require to be implemented to contain their spread and minimize SCDs.

However, there is still a limited understanding of how AI techniques can help in eliminating FNaD. We, therefore, sought to define an AI-oriented business process suited to remove the effects of FNaD on decision-making and set our research question as: “ How can firms integrate AI in their operations to reduce the impact of FNaD regarding SCDs ?” In answering this question, our study makes three contributions to the literature. First, it develops a new theoretical framework suited to mitigate the impacts of FNaD on SCDs and it analyses the relationship using a specific dataset and support-vector machine. The resulting business process manages the dissemination of information, accurately mitigating FNaD and enabling correct decision-making in regard to tackling complex issues (e.g., Jayawickrama et al., 2019 ). Second, by presenting key findings gleaned by interviewing senior managers from three different countries (Indonesia, Malaysia, and Pakistan) with expertise in supply chains, our study provides new theoretical evidence regarding how firms can avoid SCDs in emerging economies. To the best of our knowledge, our study is the first to focus on the implications and integration of AI in business processes to the end of mitigating the effects of FNaD on SCDs. Our framework thus links the supply chain and AI literature and explicates their utility in mitigating SCDs against the backdrop of fake news and disinformation campaigns. In our study, we adopted a qualitative method that involved integrating the AI literature with research on fake news to reveal how the effectiveness of decision-making can be ensured within supply chain operations. Much previous research has advanced our understanding of fake news detection mechanisms using graphs and summarization techniques (Kim & Ko, 2021 ). Furthermore, a recent study has proposed an AI-based, real-time fake news detection system by conducting a systematic literature review (Gupta et al., 2021 ). Third, our study fills a gap in the literature by providing a practical solution aimed at eliminating or reducing FNaD in business scenarios, specifically acting to minimize SCDs. The extant literature is somewhat scattered and fragmented that has not helped researchers to address many questions about FNaD (Di Domenico & Visentin, 2020 ). Our study proposes an AI-oriented business process that flags/reduces/eliminates FNaD before it can reach decision-makers and allows authentic news and information to filter through to supply chain operation resilience and prevent SCDs.

This paper is structured as follows. Section2 presents a discussion of the related literature, which is followed by an illustration of our research methodology in Sect.  3 . In Sect.  4 , the implementation details, findings, and proposed model are provided. In Sect.  5 , the implications of our model are discussed and, to conclude, future research directions are suggested.

2 Literature review

2.1 theoretical background.

Organizational Information Processing Theory (OIPT) proposes a systematic comprehension of processing and exchanging of information to increase capacities. OIPT reasons that firms need a stabilizing mechanism by possessing resources and capacities in operations to cope with uncertainties and manage unforeseen events that disturb normal business and supply chain operations (Wong et al., 2020 ). Scholarship suggests that SCDs could be caused by disinformation (e.g., Konstantakis et al., 2022 ; Xu et al., 2020 ). It is ultimately inevitable for supply chains to cultivate the capability and capacity to proactively engage the filtration of the information and news to improve supply chain operations. Firms could either opt to rely on mechanistic organizational resources for reducing their reliance on information or enhance their information processing capabilities. The more environmental uncertainty facing firms, the more information they need to gather and process to achieve better performance (Bode et al., 2011 ). OIPT proposing the primary goal of organizational-related process designs is linked with uncertainty by acquiring, analyzing, and sharing information from the business environment (Swink & Schoenherr, 2015 ; Yu et al., 2019 ). OIPT addresses the development of organizational capabilities to fill their information processing requirements (Wamba et al., 2020 ). SCDs can be avoided by the filtration of receiving accurate and timely information. Di Domenico et al., ( 2021 ) suggested that FNaD during disruptions i.e., the supply chain may cause the loss of preventable lives, misguiding information on business activities and innovation. Fact-checking measures like “know why”, “know how”, “know what”, and “know when” could be checked by emerging technologies and information processing capabilities (Jayawickrama et al., 2016 ; Swanson & Wang, 2005 ). In this perspective, AI and Machine Learning (ML) could manage the dissemination of real information by accurately detecting and mitigating false information and making correct decisions when tackling difficult issues (Endsley, 2018 ; Jayawickrama et al., 2019 ; Roozenbeek and van der Linden, 2019 ). OIPT thus focuses on linking uncertainty with information needs and information processing capacities and prescribes organizational designs to reduce uncertainty. Our study thus seeks to provide a holistic theoretical framework (integrated with AI and ML) built based on OIPT to minimize the chances of SCDs.

2.2 Artificial intelligence and supply chain operations

In academia, the concept of AI was first established in the 1950s (Haenlein & Kaplan, 2019 ). However, McCulloch & Pitts ( 1943 ) ideas on logical expression represent a notable landmark, as they led to the development of a neurocomputer design (Milner, 2003 ). While the exact year is unknown, the origins of AI can thus be dated to the 1940s; notably, to Isaac Asimov’s 1942 short tale ‘Runaround’, published in ‘Science Fiction’ magazine. In it, Asimov formalized his three laws of robotics: first, a robot cannot harm a human being; second, a robot must follow human commands; and third, a robot must defend itself (Haenlein & Kaplan, 2019 ). In 1955, in a research project on AI (McCarthy et al., 1955) Dartmouth college defined it as “ making a machine behave in ways that would be called intelligent if a human were so behaving ” (p.11). Since 1955, AI has evoked the idea of relevant human intuition and artificial machines that could stimulate the human brain and come up with environmental abstractions to work on difficult problems. During the following decade, in 1966, Joseph Weizenbaum created the famous ELIZA computer program, a ‘natural language processing (NLP) tool that was capable of holding a conversation with a human being and maintaining the illusion of comprehension. This was labelled heuristic programming and AI (Weizenbaum, 1966 ). In the 1980s, research on backpropagation in neural networks saw rapid development (Zhang & Lu, 2021 ). Under Ernst Dickmanns, Mercedes-Benz developed and commercialized a driverless vehicle fitted with cameras and sensors and an onboard computer system controlling the steering (Delcker, 2018 ). With the continuous development of AI tools, the success of IBM’s ‘Deep Blue’ chess-playing supercomputer laid the foundations for research on and the application of expert systems (Haenlein & Kaplan, 2019 ).

AI is viewed as a game-changer and as being able to facilitate both the “ abilities to self learn and a race to improve decision quality ” (Vincent, 2021 , p. 425). Kaplan and Haenlein ( 2019 ) defined AI “ as a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation ” (p.17). In supply chain management and the manufacturing industry, there has been an upsurge in AI (Kumar et al., 2019 ) that has significantly impacted operations and human roles in firms (Vincent, 2021 ; Awan et al., 2021 ) suggested that AI initiatives in firm supply chain operations can improve knowledge of the processes used to generate business performance. AI is a complex and multifaceted construct with profound implications for firm operations management (Zeba et al., 2021 ). The supply chain literature has recently emphasized the link between the application of AI and process improvement (Toorajipour et al., 2021 ). Although several AI-based supply chain applications have appeared in recent years, little research has explored their use (Riahi et al., 2021 ). While the debate on the operational outcomes of AI is still ongoing, there is little evidence in the operations management literature of how the adoption of AI may improve supply chain operations (Raisch & Krakowski, 2020 ).

Recent advancements in material and production technologies hold great possibilities for a better understanding of how to improve other manufacturing and supply chain operations (Grewal et al., 2021 ). AI-based models provide near-optimal solutions to a wide variety of routing challenges, ensuring on-time deliveries and optimizing warehouse transport (Riahi et al., 2021 ). However, little attention has been devoted to how the use of AI techniques may affect the reverse auctioning that involves supply chain partners and planning for vehicle routing and volume discount acquisition (Toorajipour et al., 2021 ). By affecting decision-making and increasing effective knowledge creation aimed at developing products customized for specific situations, AI technologies may have significant implications for a firm’s production capabilities (Awan et al., 2021 ). As a creative and frequently disruptive technology, AI facilitates the design of new products, services, industrial processes, and organizational structures that meet client needs. Further, product, service, manufacturing, or organizational processes can be designed using AI (Wamba-Taguimdje et al., 2020 ). For B2B companies, customer understanding is critical to boost products or services (Paschen et al., 2019 ). The integration of AI with the industrial Internet of Things holds significant potential for solving production-process problems and making better-informed decisions (Zeba et al., 2021 ). Early adopters of AI have created new and improved goods, which has enabled them to outperform the competition (Behl et al., 2021 ). By analyzing market intelligence, AI can uncover themes and patterns in data and may provide insights into how users creatively alter products and services (Paschen et al., 2019 ). A growing number of scholars are maximizing the influence of AI on supply chain risk management and monitoring systems to avoid SCDs (Toorajipour et al., 2021 ). However, little is known about its role in shaping monitoring, and controlling supply chain operations (Pournader et al., 2021 ). Although research has found that AI is used to improve supply chain performance, just a few AI approaches and algorithms have been explored and are used in supply chain processes (Riahi et al., 2021 ).

AI is linked to analytical, self-learning, and predictive machine learning approaches (Shrestha et al., 2019 ). These methods offer a variety of answers and prescriptive inputs to choose from when deciding how to proceed with complicated scenarios (Belhadi et al., 2021 ). Even though researchers have focused on the use of AI in different fields of study, it is important to note that very few studies have looked at how AI can be used in enhancing supply chain operations. However, the importance of AI in predicting and mitigating supply chain risk has been well established in the literature (Riahi et al., 2021 ). AI can accurately and rapidly detect relevant supply chain information by using analytics produced through AI techniques and models. They give managers a greater understanding of how each system operates and help them to discover areas in which they can improve those operations. The development of AI has made it possible to deploy predictive algorithms that allow for faster evaluations and more effective risk minimization across supply chains (Ni et al., 2020 ). The extant literature on AI argues that applying different machine learning approaches with AI can substantially decrease SCDs (Riahi et al., 2021 ). AI and ML enhance operations in many domains, including supply chain management, logistics, and inventory management (Belhadi et al., 2021 ; Ni et al., 2020 ) showed that supply chain managers can use AI to watch for and avoid incidents interrupting supply chain operations. This includes everything from the most prevalent occurrences to unknown factors such as delivery delays, quality defects, among others (Belhadi et al., 2021 ).

AI provides the opportunities and promises to move toward data-driven decision support systems. Despite the integration of AI in many firm processes, there are still challenges regarding the design of a firm supply chain that depends heavily on human contributions (Kumar et al., 2019 ). However, it has been established in the operations management (OM) literature that AI has a positive impact on various supply chain management activities (Dubey et al., 2021 ). Still, it rarely addresses how AI is applied in the OM field, such as in manufacturing, production, warehousing and logistics, and robot dynamics (Toorajipour et al., 2021 ). Even though the supply chain literature has acknowledged that many AI applications include production forecasting, supplier selection, material consumption forecasting, and customer segmentation (Toorajipour et al., 2021 ), the AI literature typically revolves around understanding effective ways to combine human intuition and decision-making (Vincent, 2021 ). The use of AI technologies gives marketers a competitive edge that reflects marketing tactics and customer behaviors (Jabbar et al., 2020 ). Customer order processing can be automated with AI, and chatbots can handle any follow-up chores (Paschen et al., 2019 ), which can increase supply chain effectiveness. It is possible to take proactive measures to combat supply chain risks by uncovering new trends in the data; this is expected to assist in achieving adaptability and higher levels of supply chain maturity (Riahi et al., 2021 ). Multiple courses of action are open to firms confronted with the risk linked to investing in AI and its positive impacts on supply chain activities. The proliferation of evolving AI technology has led to premature and conflicting conclusions regarding specific outcomes.

Scholars increasingly recognize the importance of AI in lowering downtime costs, better utilizing real-time data, better scheduling, and preserving firm operations from risks (Chen et al., 2021 ). Additionally, Chen et al., ( 2021 ) suggested a predictive maintenance framework for the management of assets under pandemic conditions, including new technologies, such as AI, for pandemic preparedness and the avoidance of business disruptions. The implementation of AI-based systems influences supply chain inventory management, “ for instance performance analysis, resilience analysis or demand forecasting ” (Riahi et al., 2021 , p.13). This raises the question of whether the use of AI systems to determine short-order policies and mitigate any bullwhip effects has been adequately addressed in the literature (Preil & Krapp, 2021 ). A review found that the adoption of AI in supply chains improves performance, lowers costs, minimizes losses, and makes such chains more flexible, agile, and robust (Riahi et al., 2021 ). Recent advances in AI help supply chain firms to enhance their analytics capabilities, leading to improved operational performance (Dubey et al., 2021 ). AI-enabled supply chain performance is becoming increasingly important to enhance financial performance; yet, no studies have been hitherto conducted to the end of gaining a better understanding of the critical antecedents of AI in driving supply chain analytics (Dubey et al., 2021 ). The direct and positive impact of AI-based relational decision-making on firm performance has been established (e.g., Bag et al., 2021 ; Behl et al., 2021 ).

2.3 Fake news, disinformation, and supply chain disruptions

Fake news (Oxford English Dictionary, 2021a) is defined as: “ false reports of events, written and read on websites. ” Furthermore, disinformation (Oxford English Dictionary, 2021b) is construed as: “ false information that is given deliberately ”. The impact of FNaD is substantial, disrupting economic operations and societal activities. FNaD also threaten brand names and potentially affect the consumption of products and services, ultimately impacting supply chain operations and demand (Zhang et al., 2019 ; Petratos, 2021 ). These are affected by panic-driven or bad decisions based on disinformation (e.g. Ahmad et al., 2019 ; Matheus et al., 2020 ; Zheng et al., 2021 ). SCD is defined as a disturbance in the flows of material, financial, and information resources between firms and their major stakeholders—e.g., suppliers, manufacturers, distributors, retailers, and customers. Disruption may affect supply chain operations for random periods (Mehrotra & Schmidt, 2021 ). Supply chains encompass the activities needed for firms to deliver products and services to their final consumers, and accurate information is an integral part of such chains, as it enables decision-makers to make decisions on future demand, supply, cash flows, returns, among other supply chain operations. There are historical examples of how FNaD can affect the supply chain and business operations. From the 1950s to 1990, the tobacco industry constantly shared disinformation on the adverse effects of active and secondhand smoke exposure by manipulating research, data, and the media (Bero, 2005 ; Dearlove et al., 2002 ). In September 2006, the Royal Society, Britain’s premier scientific academy, wrote to ExxonMobil urging it to stop funding the dozens of groups spreading disinformation on global warming and claiming that the global temperature rise was not related to increases in carbon dioxide levels in the atmosphere (Adam, 2006 ). In 2013, the Associated Press official Twitter account was hacked and a tweet was made about two explosions injuring President Barack Obama; within hours, this wiped US$130billion from the stock market (Parsons, 2020 ; Tandoc et al., 2018 ), which affected stock supply chain operations. In 2017, six UK Indian restaurants fell victim to fake news stories claiming that they were serving human flesh (Barns, 2017 ; Mccallum, 2017 ). One restaurant had to cut staff hours and saw its revenue fall by half (National Crime Agency, 2018 ). Such events could also have indirect effects on supply chain operations, ultimately being conducive to SCDs.

The persuasive power of fake news could continuously damage global supply chains, such as meat, vegetables, fresh food, and fruits in different parts of the world (Xu et al., 2020 ). All businesses have felt the rapid dissemination of false information and propaganda among suppliers and distributors. Many countries such as France, Germany, India, and the US imposed restrictions on products from entering and leaving different countries due to disinformation and fake news about Covid-19 (Xu et al., 2020 ). Moreover, several fake social media posts or misinformation about the U.S. food plant fire caused the food supply disruption. The USDA told Reuters via email that it is not true that these fires were started on purpose (Reuters, 2022 ). Due to the widespread false information about COVID-19, there was an epidemic of methanol poisoning. It is claimed that 796 Iranians lost their lives to the alcohol intoxication after reading online claims that alcohol may treat their illnesses (Mahdavi et al., 2021 ). This echoed the rapid dissemination of false information regarding COVID-19 on social media at the outbreak’s onset and disrupted the supply of many food items (Mahdavi et al., 2021 ). Some people falsely claim that using alcohol to rinse the mouth and avoid COVID-19 infection works (Delirrad & Mohammadi, 2020; Soltaninejad, 2020). The global supply chains have been shaken by the widespread of fake news causing widespread disruptions and affecting firms’ reputations. The effects of fake news on COVID-19 are still being felt by supply chains across many sectors, and it has irrevocably affected long-term supply chain strategies.

In more recent times, the COVID-19 pandemic has spawned high volumes of FNaD. One example of disinformation pertaining to a COVID-19 remedy involved a herb named ‘ Senna Makki ’ in Pakistan., Someone started sharing on social media as a cure for the virus, which caused escalating demand and an increase in price from 1.71USD to 8.57-11.43USD per kg within two months (The News, 2020 ). This kind of fake news could affect vaccine supply chains. In the stock market, Clarke et al., ( 2020 ) revealed that a well-known crowd-sourced content service for financial market websites had been generating fake news stories due to the editors’ lack of ability to detect them. This attempted use of fake news had ‘widespread short-term implications for the financial markets. The Kroll global fraud and risk report of 2019/20 shared an incident of fake news in the banking industry. The rival institution purchased an African bank, the purchaser was confronted with a negative social media campaign, fabricated news, and stories, and manipulated closed-circuit television footage (Booth et al., 2019 ). These examples demonstrate how FNaD can disrupt supply chains and business operations.

Our review of past studies yielded Table  1 , which summarizes key studies interlinking AI, SCDs, and FNaD. These studies are selected from top-ranked journals (e.g., CABS 3 ranked and above) published in the last three years. The lack of research on all three aspects is very clear, with no studies emphasizing their combination. Our study significantly contributes to bridging this gap.

3 Methodology

We used mixed methods, the AI and ML-driven method, and our case study interviews to further validate our model. The following procedure was used to execute the AI and ML-driven method for data analysis. (1) The Dataset Enrichment was based on two techniques—i.e., Porter stemmer (PS) and Term Frequency-Inverse Document Frequency (TFIDF). (2) Query Expansion was utilized for natural language processing (NLP) to precisely predict the accuracy of fake news. (3) The Support Vector Machine (SVM) classifier was utilized to train the model and then finally evaluate fake and real news outcomes for effective decision-making SCDs. Table  2 provides further justification for this approach. The studies also used other measures such as precision, recall, and accuracy and we also integrated similar measures in our analysis.

Secondly, the involved cases for interviews were from Indonesia, Malaysia, and Pakistan. These countries share similarities. Furthermore, these are emerging countries with common economic and political ties as well as government-to-government contacts. Similarly, these nations are making strides toward digitization and AI. Numerous previous studies (e.g., Atkin et al., 2017 ; Ghazali et al., 2018 ; Rahi et al., 2019 ; Siew et al., 2020 ) also used targeted populations from these countries to conduct studies on comparable issues. The case study method was best suited to achieve the objectives of our study, considering the explanatory nature of the research question (Eisenhardt & Graebner, 2007 ; Yin, 2014 ) and the fact that this is an emergent research area to be combined with modern methodological innovations such as ML (Gupta et al., 2021 ; Kovács & Sigala, 2021 ; Sodhi & Tang, 2021 ; Sheng et al., 2020 ). Further, multiple case studies are assumed to be more reliable because they enable phenomena to be observed and studied in many contexts—thereby helping to provide replication logic for particular cases, which would otherwise be viewed as independent (Yin, 2014 )—and because they are useful for theory development (Eisenhardt, 1989 ).

A goal-directed sampling technique—i.e., an incremental selection method (Denzin & Lincoln, 2005 )—was employed to investigate the flagging/reducing/eliminating process of FNaD. This technique is effective for the collection of both qualitative and quantitative data because the sample is purposely chosen based on the project’s unique requirements and the evaluator’s judgment (Polit & Beck, 2012 ; Vos et al., 2011). The data acquired using this approach and technique tend to be of a high standard only if the participants are willing and able to provide accurate information that will enable the researcher to gain a thorough knowledge of an experience. We thus adopted this sampling technique because our target key management functionaries would be the participants best suited to furnish the essential data for our study (Creswell, 2014 ), providing useful insight into what works and what does not in terms of its theoretical and technical components. Any case variations were scrutinized based on the industry to achieve a better understanding of FNaD impacts.

To conduct the interviews, we approached 33 firms—36.36% Malaysian, 36.36% Pakistani, and 27.28% Indonesian ones. Eventually, a total of 16 firm representatives participated in our study—six each from Malaysia and Pakistan, and four from Indonesia. According to Teddlie & Yu ( 2007 ), this was a sample size sufficient to produce narrative records adequate to provide viewpoints directly relevant to the topic under study. The sample firms were small and medium-sized, with staff numbers ranging from 18 to 183. Small businesses have learning systems that are complex and dynamic and geared to generate the efficiency needed to sustain such firms in the market (Zhang et al., 2006 ). Therefore, an in-depth investigation was needed to understand the stances of small and medium-sized firms about FNaD. Fifteen semi-structured in-depth interviews were prepared, based on the interviewees’ understanding of their respective firms and the inflow and outflow information features such as sources, routines, systems, and processes. The interviewees were owners, chief executive officers, directors, and associated top managers. The participants’ varied perspectives reduced dependency on a single participant’s perspective, enriching the data obtained. We also used project reports, operational policies, and other relevant documentation to identify and triangulate themes during the data analysis (Yin, 2014 ). Due to the COVID-19 pandemic, 90% of the interviews were conducted over the internet (e.g., GoogleMeet) and any observations were recorded and reviewed later.

We followed the interview protocol to integrate the philosophy, processes, and questions of the study to attain reliability (Frost et al., 2020 ; Ponterotto, 2005 ). To delve deep into the situation about the impacts of fake news and any remedial actions, we used relevant prompts in open-ended questions. The extensive conversations provided key findings regarding the solutions adopted to counter the effects of fake news on business and supply chain operations by reflecting on three industry and technology developments, gathering din depth and valuable narratives in the process. All interviews were audio-recorded, converted into MSWORD, and thematically analyzed via NVIVO 12. To ensure validity and reliability, each researcher independently coded the responses given to the open-ended questions to fully grasp any concepts that were not readily provided by existing theories or field research. The answers were discreetly coded to fully understand any new sentiments, knowledge, and opinions that may not be available in the literature on the selected industries and countries. This practice provided an AI-based solution to fake news issues in business. Furthermore, other consistency checks were carried out, whereby the data and preliminary interpretations were presented to the interviewees from whom they had been sourced to determine their credibility and incorporate any necessary changes, and the scripts were then finalized following their approval (Merriam, 2009 ).

4 Implementation procedures, findings, and proposed model

4.1 ai and ml implementation, 4.1.1 obtaining the dataset.

The dataset for the AI and ML approach was drawn from four Pakistani major online news sources—i.e., ‘Geo News’, ‘The Dawn’, ‘Express Tribune’, and ‘The News’. Approximately 500 pages from each source were scrutinized to extract the relevant affairs and topics from January to April 2021. SCD data were divided into natural, human-caused, maritime, and mass disruptions related to FNaD. Table  3 provides some examples.

The words were used in different contexts. For example, Words such as health, vaccine, Covid-19, and pandemic were mostly used in the main text in articles related to health supply chains and their disruptions, and words like political, freedom, rights, democracy, and military were applied in political articles. We followed a step-by-step procedure for the implementation.

4.1.2 Pre-processing dataset

• PS (Porter Stemmer) was used to index each news page/article to filter out any stop, repeated, and common words to avoid noise in the dataset. The algorithm was used, over several rounds, to remove any non-relevant words from the datasets/textual scripts before considering all criteria or defined rules (Zhang et al., 2020 ). Such an algorithm has been proven to be one of the best techniques in terms of performance (Joshi et al., 2016 ).

• TDIF (Term Frequency - Inverse Document Frequency) was utilized for the classical ML models. TDIF is a text classification technique utilized for organizing textual documents from raw datasets into predefined categories to obtain useful information. This is done by representing textual documents into feature vectors consisting of weights that indicate the contribution of each term in text classification (Deng et al., 2004 ; Dogan & Uysal, 2019 ). The effectiveness of TDIF has also been proven to be significant in the weighting process (Dogan & Uysal, 2019 ).

4.1.3 Dimension reduction and features engineering

The Query Expansion.

In the field of natural language processing (NLP) and information retrieval such as metrics of text semantic similarity are the most used techniques (Zhu et al., 2018 , 2020 ; Gao et al., 2015 ). The query expansion approach was utilized for natural language processing to precisely compute the relevancy of keywords related to FNaD by calculating the semantic distance between keywords related to SCDs. This phase explained the keywords used in SCDs to train the classifier to predict the appropriateness of SCDs in the news. As a feature, we analyzed the significance of each SCD keyword to a news category. We had the news articles in the business category and we wanted to evaluate whether it is appropriate for SCDs. The degree of appropriateness was based on the feature’s relevancy score; +1 perfectly appropriate and − 1 vice versa. The outputs of fake or real were decided based on the scores. Each keyword pair of news category and supply chain disruptions were considered appropriate if the relevancy score is + 0.65. As a result, we trained the SCD keywords appropriateness to predict based on the remaining dataset. We trained various frequently used classifiers and reported SVM results as SVM outperformed others.

Query Expansion: The query expansion approach was utilized for natural language processing (NLP) to precisely compute the relevancy of keywords related to FNaD by calculating the semantic distance between keywords related to SCDs, queries, and news articles. This phase explains the keywords used in SCDs to train a classifier to predict the appropriateness of SCDs in the news. We analyze the significance of each SCD keyword to a news category. We have news articles in the category “Business” and we wish to evaluate whether it is appropriate for a SCD example such keywords in the category; “natural disasters”, “man-made disasters”, “marine incidents”, and “mass trauma incidents”. We consider the degree of appropriateness of the news category with each of the SCD keywords based on the feature’s relevancy score. In this scenario, -1 represents that a news item with the category “sports” is utterly inappropriate to be examined with supply chain disruption keywords “offshore oil rig mishaps,“ but a + 1 score suggests that it is perfectly appropriate. Table  4 shows the examples of the relevancy score of the news category against supply chain disruption keywords present in our database.

The WordNet ontology (Leão et al., 2019 ) was utilized here for calculating semantic differences between the multiple keywords. The semantic similarity analysis is performed to determine the degree of semantic similarity between the texts. In the fields of NLP, natural language understanding (NLU), and information retrieval, such metrics of text semantic similarity are the most used such as WordNet ontology as a linguistic source because of its wide vocabulary and the explicit definite semantic hierarchy (Zhu et al., 2018 , 2020 ; Gao et al., 2015 ).

4.1.4 Determine the ML task and position the dataset

Classifier Training: The problem of SCD appropriateness to a news article is formulated into two possible outcomes “fake” or “real” as a binary classification. The training set was created from the randomly selected news articles from the dataset. To interpret the training dataset, a supervised learning task was performed with human assistance to make machine learned if a news category “world” is appropriate in supply chain disruption keywords such as “Wildfires”, “Political Crises”, and “Mass migration”. Each keyword pair of news category and SCDs were considered appropriate if the relevancy score is + 0.65. We trained the SCD keywords appropriateness on the selected articles in the training set to predict the appropriateness scores in the test set. We trained numerous frequently used classifiers to train the proposed model, however, because Support Vector Machines (SVM) produced the best results, therefore, we only provide the findings of the SVM classifier in this study.

4.1.5 Machine learning technique and interpreting results

Support Vector Machine: We develop a model for each news item by training a binary classifier using two possible outcomes positive (real) and negative (false) news with the appropriateness of SCDs. We chose a binary classification because it is assumed that a responsible user will want to verify the news before spreading it. In order to determine whether the news is real or fake, the user would most likely check online sources (databases/publishers), and in the process, the user may be able to verify or refute the information depending on the source. Subsequently, if the model is trained using a supervised learning technique with only two possible outcomes, the model is forced to make a binary decision, this increases the model’s accuracy significantly. Therefore, the Support Vector Machine (SVM) (Cortes & Vapnik, 1995 ) classifier was utilized to train the model. SVM is based on a binary classification model, which divides the training samples into two further classes based on multiple support vector hyperplanes in a vector space (Melki et al., 2017 ; Tharwat, 2019 ). The supervised learning approach such as SVM is a widely used machine learning method utilizing training examples or datasets to train the model that can be used to solve classification, and regression problems (Melki et al., 2018a , b ).

The metrics we utilized for evaluation were; Mean Reciprocal Rank (MRR) (Ghanbari & Shakery, 2019 ), Precision at 5 (P@5) (Sharma et al., 2020 ), and Normalized Discounted Cumulative Gain at 5 (NCDG@5) (Alqahtani et al., 2020 ). A threshold was used for the 5 best matches and the top match was given as the output. These evaluation measures account for the testing accuracy of the constructed model. The comparison between the training set and test set values for MRR, P@5, and NCDG@5 depicted slight differences, this validates the accuracy of the model in the test set with 0.647, 0.656, and 0.511 respectively.

4.2 Interview-based validation and proposed model

The impact of fake news on supply chain operations emerged as the first theme from the analysis. When asked about their knowledge and understanding of FNaD, the respondents gave detailed replies. Some argued that it is one of the most harmful aspects of the internet, with the potential to create SCDs. Respondent 4 said that “ Internet information makes us more attentive. ” Respondents 1, 5, 9, 11, and 13 shared the negative impacts of FNaD on quick, routine, and time-consuming decision making: “ Quick decisions based on the inclusion of the FNaD could be a transcendent disaster for any firm’s supply chain ” (Respondent 5). “ if FNaD is included in routine or prolonged decision making, it definitely agonizes the future result ” (Respondent 2), and “If any decision is based on lies, how can someone expect positive consequences? ” (Respondent 11). Two respondents shared that the purpose of spreading FNaD is to create a specific mindset and narrative in the economy/market to manipulate it: “ Misleading information supposed to build a specific narrative and sentiment in the market, enemies and indirect competitors usually involve in it ” (Respondent 1). The respondents indicated that fake news directly affects operations and has an indirect influence on supply chain operations, contributing to SCDs.

Dealing with FNaD emerged as the second theme of the analysis. The respondents provided numerous options in this regard, suggesting that global communities, social media sites, government, technology, and top management can play key roles in countering FNaD. FNaD should be dealt with smoothly and promptly; otherwise, it will negatively affect supply chain performance: “ As a business community, together we should deal with misleading information and news. Otherwise it could become a scar in business performance ” (Respondent 16). Respondents 12, 15, and 16 mentioned that FNaD should be dealt with as a pandemic like the COVID-19 one. Some respondents mentioned the names of the entities they considered to be primarily responsible for keeping FNaD under control, with Respondents 8, 2, and 11 pointing at the global internet community, social media sites, and the government. On the other hand, respondents 1, 3, 9, 10, 12, and 13 believed that the responsibility of curbing the effects of FNaD on supply chain performance and decision-making should fall on specific industries and businesses. Respondent 13 further explained that “as a business entity, we need to find a mechanism which guides us that specific news or information is legit or not ”, while Respondent 6 opined that “in today’s world, if your business isn’t data-driven, then you are definitely living in the jungle.”

FNaD filtering and counter modeling process suggestions and preparations became the third theme of the analysis. This enriched session contributed many insights into and inputs about countermeasures to FNaD in business and supply chain operations. When we asked Respondent 7 about this, he shared the following Bill Gates quote “ The world won’t care about your self-esteem. The world will expect you to accomplish something before you feel good about yourself ”, and further added “As a business caretaker, this is my responsibility is to shelter and protect my company from fake news, so that, at the end of the day, I will have no regrets.” Respondents 3, 5, 7, 10, 14, 15, and 16 suggested that AI will provide solutions suited to control and counter FNaD. Respondent 10 advised that “Data crawler integration with AI could provide a solution to FNaD”. Respondent 11 shared a similar thought “Each government should prepare AI-based processes according to specific society and economy to rectify the impact of fake news, and that process in the form of software should be provided free of cost to businesses”. The participants also highlighted the importance of using multiple sources to determine whether the news is fake or real, as a single source could be biased or politically driven. Based on the procedure applied for AI, the SVM, and the interview-based validation, we proposed the FNaD detection model shown in Fig.  1 , which encapsulates the key findings.

A fake news and disinformation detection model that uses AI and ML

As depicted in Fig.  1 , the practical decision-making for SCDs is characterized by the predominant use of experiences, judgments, and multiple media resources. These can be categorized as real and fake news. Data demonstrates that the severity of the fake news impact is prompting businesses to invest in more robust, collaborative, and networked supply chains and should prepare AI-based processes according to specific societies and economies to rectify the impact of fake news. Datasets from multiple sources teach decision-makers about whether the particular news or information is legit or not. The data from multiple sources allows decision makers to apply the machine learning approaches and use artificial intelligence. They can therefore better select the appropriate mechanisms to detect fake and real news.

5 Contributions, implications, conclusion, and future research directions

5.1 contributions and theoretical implications.

Our study fills the knowledge gap about SCDs by utilizing AI and ML that assist to act against FNaD affecting supply chain operations. Loureiro et al., ( 2020 ) suggested that AI has diverse applications in several industrial domains. Dolgui & Ivanov ( 2021 ) hinted that AI could assist in improving resilience against and mitigation of SCDs. We combined a case qualitative method, AI, and SVM in order to reveal how effective decisions could be made within supply chain operations. The extant research advanced our understanding of fake news detection mechanisms using graph and summarization techniques (Kim & Ko, 2021 ). Furthermore, a recent study proposed an AI-based real-time fake news detection system by conducting a systematic literature review (Gupta et al., 2021 ). Our study is novel and distinct from the previous ones in that it developed an effective decision-making model for SC firms to avoid any disruptions caused by FNaD. As such, it contributes to the SCDs literature that will be of interest to scholars and practitioners.

Additionally, the study bridges a gap in the literature by providing a practical solution suited to eliminate FNaD in business scenarios affected by SCDs. The scattered and fragmented extant literature had left many questions about FNaD unanswered (Di Domenico & Visentin, 2020 ). Therefore, the main contribution of our study is to propose an AI- and ML-oriented process capable of flagging/reducing/eliminating FNaD before it reaches decision-makers and of identifying any authentic news and information, thus counteracting SCD-aimed news.

The United Nations ( 2020 ) has urged the implementation of actions against misinformation and cybercrime. Edwards et al., ( 2021 ) concluded that such ‘digital wildfire’ spreads faster than original and legit news. We propose a process, named FNaD integrated with AI that initiates when news or information is embedded in it. It then begins verification within defined sources (e.g., major newspapers’ websites) and, in the next step, it starts seeking similarities between news or information keywords. Once the AI process reaches a decision, it provides an output by classifying the news item as FNaD (rejection) or real/ authentic news or information (acceptance).

FNaD can be significant determinants of SCDs, as is highlighted in research (Kovács & Sigala, 2021 ). They adversely influence firms’ operations, import, and export, and alter purchasing behaviors (e.g., Di Domenico et al., 2021 ; Petit et al., 2019 ; Wang et al., 2021 ). The FNaD model shows the ability to control the inclusion of FNaD into firms’ activities. Our study contributes to the management and detection of FNaD in firms’ supply chain operations by proposing and testing a FNaD detection model that uses AI and ML. This model could help to control the potential digital wildfire before it damages firms’ operations. FNaD create unnatural phenomena that interrupt supply chain operations and enhance demand-supply loopholes (e.g., De Chenecey 2018 ; Dwivedi et al., 2020 ).

5.2 Managerial and policy implications

Our model detects FNaD early before they can affect firms or managerial decision-making. The current pandemic scenario has turned the attention of managers and governments toward FNaD and their impacts on supply chain operations, economy, and society. On the other hand, with AI and ML becoming an integral part of firms and operations, managers should consider their adoption to deal with FNaD, given their potential to detect and filter them out. Our model is executed and managed based on major local databases and news outlets to support supply chain operations. Should managers wish to integrate adding any further international data and news outlets, they could do so based on their requirements. The implementation of our model would depend on a willing and authoritative IT infrastructure, with even small and medium enterprises being able to invest in its application. We proposed a process capable of detecting and filtering out FNaD. This process protects firms from the impacts of FNaD, enabling managers to engage in decision-making based on legitimate and valid news or information.

From the perspective of specific industries, newsrooms could utilize the FNaD detection model to confirm a news item from different sources. In other words, the FNaD detection model can help in the timely development of a counter-strategy by detecting any fake news before it spreads and causes SCDs. The phenomenon has recently been seen in the context of the COVID-19 pandemic, with people sharing unverified news items on the virus and the side effects of vaccines over social media, thus causing SCDs in vaccine distribution. Moreover, pre-emptive fake news detection can be equally beneficial in avoiding financial market crashes. For government policymakers, the FNaD detection model can be a comprehensive tool to be used during pandemics or similar situations. Governments have been seen to regularly change their decisions, rules, and regulations. Therefore, at the government level, the FNaD detection model can ensure that accurate and on-time legitimate information is received to deal with any economic, social, and health conditions. Another implication for governments pertains to the provision of this process—for free or at a discount—to all business-related entities, especially micro, small, and medium firms. Such a decision would create trust between the government and those entities.

5.3 Conclusion and future research directions

SCDs are problematic for business operations. It is believed that SCDs could cause obstacles due to disinformation. Therefore, we proposed the FNaD model that filters the FNaD by utilizing AI and ML. This model takes help from different sources on internet to verify the received information. It then decides and notifies whether that received news is authentic or not. By using a mixed-method approach, we proposed a way to tackle SCD-creating FNaD using AI- and ML-based techniques. In this regard, future research could, first, focus on more specific FNaD and supply chain operation case studies, such as the detection of FNaD in humanitarian operations using AI and ML approaches. Additionally, they could integrate specific operational performance measures in these approaches, combining them with advanced visual methods. Also, given the fast pace of scientific development, any new and effective algorithm or technique could be used in the proposed model in the future. Furthering, testing the model based on longitudinal studies aimed at exploring and understanding the developments in SCDs linked with FNaD would make it more reliable and refined.

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Akhtar, P., Ghouri, A.M., Khan, H.U.R. et al. Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions. Ann Oper Res 327 , 633–657 (2023). https://doi.org/10.1007/s10479-022-05015-5

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XiaoxiaoMa-MQ / Awesome-Deep-Graph-Anomaly-Detection

Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contribut…

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This repo is a collection of AWESOME things about fake news detection, including papers, code, etc.

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Vatshayan / Fake-News-Detection-Project

Final Year Fake News Detection using Machine learning Project with Report, PPT, Code, Research Paper, Documents and Video Explanation.

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ICTMCG / FakeSV

Official repository for "FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms", AAAI 2023.

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Paper list of misinformation research using (multi-modal) large language models, i.e., (M)LLMs.

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ni9elf / 3HAN

Official implementation of "3HAN: A Deep Neural Network for Fake News Detection" (ICONIP 2017)

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nguyenvo09 / EMNLP2020

This is official Pytorch code and datasets of the paper "Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News", EMNLP 2020.

• Updated Nov 12, 2022

RMSnow / WWW2021

Official repository to release the code and datasets in the paper "Mining Dual Emotion for Fake News Detection", WWW 2021.

• Updated Dec 31, 2021

ICTMCG / News-Environment-Perception

Official repository for "Zoom Out and Observe: News Environment Perception for Fake News Detection", ACL 2022.

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Nicozwy / CofCED

COLING 2022: A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection.

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Official repository for "Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection", AAAI 2024.

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neemakot / Health-Fact-Checking

Dataset and code for "Explainable Automated Fact-Checking for Public Health Claims" from EMNLP 2020.

• Updated Apr 27, 2021

ICTMCG / M3FEND

Official repository for "Memory-Guided Multi-View Multi-Domain Fake News Detection", IEEE TKDE.

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neemakot / Fact-Checking-Survey

Repository for the COLING 2020 paper "Explainable Automated Fact-Checking: A Survey."

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[WWW 2022] The source code of "Evidence-aware Fake News Detection with Graph Neural Networks"

• Updated Apr 23, 2024

nguyenvo09 / EACL2021

This is the PyTorch code + data repository for paper "Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection", EACL 2021

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✨ Fake news classification using source adaptive framework - BE Project 🎓The repository contains Detailed Documentation of the project, Classification pipeline, Architecture, System Interface Design, Tech stack used.

• Updated Apr 16, 2023

ICTMCG / ENDEF-SIGIR2022

Official repository for "Generalizing to the Future: Mitigating Entity Bias in Fake News Detection", SIGIR 2022.

• Updated May 29, 2022

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Fake News Detector Web Application

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YouTube is making tools to detect face and voice deepfakes

It plans to launch a pilot program for the voice detection tool by early next year..

YouTube is developing new tools to protect artists and creators from the unauthorized use of their likenesses. The company said on Thursday that new tech to detect AI-generated content using a person’s face or singing voice is in the pipeline, with pilot programs starting early next year.

The upcoming face-detection tech will allegedly let people from various industries “detect and manage” content that uses an AI-generated depiction of their face. YouTube says it’s building the tools to allow creators, actors, musicians and athletes to find and choose what to do about videos that include a deepfake version of their likeness. The company hasn’t yet specified a release date for the face detection tools.

Meanwhile, the “synthetic-singing identification” tech will be part of Content ID , YouTube’s automated IP protection system. The company says the tool will let partners find and manage content that uses AI-generated versions of their singing voices .

“As AI evolves, we believe it should enhance human creativity, not replace it,” Amjad Hanif, YouTube’s vice president of creator products, wrote in a blog post. “We’re committed to working with our partners to ensure future advancements amplify their voices, and we’ll continue to develop guardrails to address concerns and achieve our common goals.”

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