Fake News, Towards Being a CRITICAL Netizen

Source: How to Spot Fake News.Toronto Public Library (TPL) Online. https://www.kdnuggets.com/2017/10/guide-fake-news-detection-social-media.html. Retrieved: 08 Feb 2021

What is Fake News?

The New York Times defined “fake news” on the Internet as false articles deliberately fabricated to deceive readers, generally with the goal of profiting through clickbait. Clickbait is content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page.

PolitiFact described fake news as fabricated content designed to fool readers and subsequently made viral through the Internet to large groups of people who further disseminate it.

Fake News and Social Media

Social media platforms like Facebook and Twitter enable information sharing among their users, and many of these platforms present ‘news’ items, ads or ‘sponsored content’ in a manner that makes it difficult to distinguish real news sources from spoofed sites, or hoax sites. Most social media platform ad space is sold through brokers, meaning the platform often has no idea what is being advertised on their site. These characteristics make social media platforms an ideal place for fake news to flourish.

How to Spot Fake News


  • Who wrote it? Check for the author’s name. Is the name available or is it missing? Most authors who put time into a well-researched article will likely have their name attached to it.
  • What are their qualifications? If the author’s name is listed, find out who the person is and what their credentials are. Do a search on the author’s name, find their occupation and other articles written by them. Is the author an expert in the field? Does the author work at a reputable organization? Are the articles well-researched?
  • Check the “About Us” section. On the top or bottom of the website there should be a section called “About Us.” This section outlines the purpose of the website. Does the organization have an authoritative team of journalists or writers? Or do they invite members of the general public to contribute? Reading about the host of the website will help you determine whether it is a trustworthy source.
  • Does the article inform you of all sides of the topic? News articles should provide you with facts from various viewpoints. If the article showcases only one side of the argument, readers should keep in mind that they are not seeing the full story and the article may contain bias.      
    Tips: Check for sources cited in the article that support the claims in the story. Search for the sources online. Are they reliable sources? Do they support the claims being made? Are direct quotes used and are they taken out of context?

  • Does the content match the headline of the article? A headline should provide you with an idea of what the entire article is about, but it can also be used to persuade you to believe something before reading the article. Authors may use this to their advantage and falsify their headlines to get people to read the full article or believe the claim without reading the article.     
    Tips: In addition to the headline, check for any spelling or grammatical errors in the text. Well-researched articles are typically read and re-read before posting.


  • When was this article published? Older articles may not contain up-to-date facts and might have broken links. Individuals sharing an older article may discover that some information has been disproven or debunked.                                 
  • Was the article repurposed or updated? Repurposed or updated content tends to have a disclaimer at the beginning or end of the article. News organizations may repurpose an article if a current event is related.                                 
  • How important is the date? The date gives you an indication of when the article was published. Websites may show time/date stamps in the article, but it is possible that these could be modified.
    Tips: Run a search to see if there are similar articles written by other news organizations.


  • Does this web address (URL) look correct? Typing in the wrong web address will direct you to a webpage that you were not intending to visit. It may lead you to a page with computer viruses. Be cautious of website URLs that are made to look official or real. A splashy looking website can contain fake news. Similar to a phone number, a minor mistake can take you to a completely different website.                         
    With few exceptions URLs including their domains (.ca, .com, etc.), can be purchased by anyone. Many domains do not have any requirements to register. Some individuals trick users by using domain names to imitate an organization’s official site.
    Tips: If you do not know the URL, use a search engine and review the results for the result you are looking for.

  • Did I find this on Social Media? Social Media platforms are not news organizations. These are platforms for people to create and/or share content. Monitoring of fake news is virtually non-existent on social media platforms and blogs. They use algorithms to curate content that would be of interest to you, creating a personal echo chamber. Be cautious of videos/photos as images may have been manipulated.       

Note: While photo/video editing software allows filmmakers and artists to create life-like environments, this software also provides anyone the same tools to manipulate an image/video to fit their story. Use Google reverse image search to see where else an image has appeared.

  • Did I find this on a blog/website? Blogs contain content written informally and run by an individual or small group. Anyone can register for a blog or create a website. Websites and blogs may use sensational headlines to pique your interest. Individuals can generate advertising revenue from page views. They may write articles from a certain viewpoint to target specific audiences. Be cautious of websites that use strong language to generate a click/reaction.
    Tips: Verify the information you found by using another website. Find the original source of the information. Be aware that individuals may post their fake news on a similar website.

  • Did I find this in the news media? Newspapers and network/cable news hire reporters and journalists to gather and report on news. These news organizations adhere to strict policies and standards. They include an online presence to report on breaking news. News media may also have opinion pieces or discussions with individuals offering different viewpoints on current topics.


  • What is the purpose of the information? To inform? Is the article or online content informative in some way? What information is it giving you? Try to think critically about the information you receive. Be skeptical! Can you verify the facts? Are sources offered? Can you evaluate the sources? If there are links on the page, where do they take you?
    To sell? Is the article trying to sell you something? Some online articles are designed to get you to buy a product. Sometimes, what looks like a news article is actually an advertisement. Sometimes, they exist side by side, news articles next to “sponsored content” or “native advertising.” Can you tell the difference?
    To entertain? Satire and fake news are not the same thing. Satire uses strategies such as exaggeration or irony to expose hypocrisy, especially in current affairs, and it is (usually) funny. Fake news *is* that hypocrisy, and nobody’s laughing.
    To persuade? It is not at all unusual for an author to try to persuade their audience to believe one thing or another. Can you tell when something is persuasive? Ask yourself, what is the author’s point of view? Is it objective? Is it biased? Then ask yourself why the author might have that point of view, and why they might want you to think one way or the other. Who benefits from you thinking that way?
    What other reasons can you think of that someone might want to spread information, or misinformation?
    Tips: Ask yourself: who benefits?

Source: Shu, Liu (2017).  A Quick Guide to Fake News Detection on Social Media. KD Nuggets Online. https://www.kdnuggets.com/2017/10/guide-fake-news-detection-social-media.html. Retrieved: 08 Feb 2021

Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information allow users to consume and share the news. On the other hand, it can make viral “fake news”, i.e., low-quality news with intentionally false information. The quick spread of fake news has the potential for calamitous impacts on individuals and society. For example, the most popular fake news was more widely spread on Facebook than the most popular authentic mainstream news during the U.S. 2016 president election. Therefore, fake news detection on social media has attracted increasing attention from researchers to politicians.

Fake news detection on social media has unique characteristics and presents new challenges. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult to detect based on news content. Thus, we need to include auxiliary information, such as user social engagements on social media, to help differentiate it from the true news. Second, exploiting this auxiliary information is nontrivial in and of itself as users’ social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. This quick guide is based on a recent survey [1] that presents issues of fake news detection on social media, state-of-the-art research findings, datasets, and further directions. Next, we will highlight the major perspectives for this survey.

Characterization and Detection

Figure 1 is an overview of detecting fake news on social media, including two phases: characterization and detection. Fake news itself is not a new problem, and the media ecology has been changing over time from newsprint to radio/television, and recently online news and social media. The impact of fake news on traditional media can be described from the perspective of psychology and social theories.  For example, two major psychology factors make consumers naturally vulnerable to the fake news: (i) Naïve Realism: consumers tend to believe that their perceptions of reality are the only accurate views. (ii) Confirmation Bias: consumers prefer to receive information that confirms their existing views. As another example, social identity theory and normative influence theory describe that preference for social acceptance is essential to a person’s identity, making people choose “socially safe” option for consuming news, even the news being shared is fake news.

Fake news on social media has its unique characteristics. For example, malicious accounts can be easily and quickly created to boost the spread of fake news, such as social bots, cyborg users, or trolls. In addition, users are selectively exposed to certain types of news because of the way news feed appear on the homepage in social media. Therefore, users on social media tend to form groups containing like-minded people where they are likely to polarize their opinions, resulting in an echo chamber effect.

Figure 1. Fake news detection on social media: from characterization to detection

 The aforementioned theories are valuable in guiding research of fake news detection. Existing algorithms for fake news detection can be generally categorized as (i) News Content Based and (ii) Social Context Based.

  • News content based approaches focus on extracting various features in fake news content, including knowledge-based and style-based. Since fake news attempts to spread false claims, knowledge-based approaches aim to using external sources to fact-check the truthfulness of the claims in news content. In addition, fake news publishers often have malicious intents to spread distorted and misleading, requiring particular writing styles to appeal to and persuade a wide scope of consumers that are not seen in true news articles. Style-based approaches try to detect fake news by capturing the manipulators in the writing style.
  • Social context based approaches aim to utilize user social engagements as auxiliary information to help detect fake news. Stance-based approaches utilize users’ viewpoints from relevant post contents to infer the veracity of original news articles. In addition, propagation-based approaches reason about the relations of relevant social media posts to guide the learning of credibility scores by propagating credibility values between users, posts, and news. The veracity of a news piece is aggregated by the credibility values of relevant social media posts.


Even though online news can be collected from different sources, manually determining the veracity of news is a challenging task, usually requiring annotators with domain expertise who performs a careful analysis of claims and additional evidence, context, and reports from authoritative sources. Existing public datasets of fake news are rather limited due to these challenges. To facilitate the research for fake news detection, this survey [1] provides a usable dataset, named FakeNewsNet, which includes news content and social context features with reliable ground truth fake news labels.

Promising Future Research

Fake news detection on social media is a newly emerging research area. The survey [1] discusses related research areas, open problems, and future research directions from a data mining perspective. As shown in Figure 2, research directions are outlined in four perspectives: Data-oriented, Feature-oriented, Model-oriented, and Application-oriented.

Figure 2. Future directions and open issues for fake news detection on social media

  • Data-oriented: it focuses on different aspects of fake news data, such as benchmark data collection, psychological validation of fake news, and early fake news detection.
  • Feature-oriented: it aims to explore effective features for detecting fake news from multiple data sources, such as news content and social context.
  • Model-oriented: it opens the door to build more practical and effective models for fake news detection, including supervised, semi-supervised and unsupervised models.
  • Application-oriented: it encompasses research that goes beyond fake news detection, such as fake new diffusion and intervention.

[1] Shu, K., Sliva, A., Wang, S., Tang, J. and Liu, H., 2017. Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, 19(1), pp.22-36.


Instead of the usual thought-provoking questions, I challenge you to watch Vera Files’s Pramis, Walang Lokohan: Six-part video tutorial series sa online verification ( https://verafiles.org/learning-corner)

About Vera Files
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Create an awareness post about fake news and misinformation.


1. Share on your FB feed an example of fake news. It can be local or global news.
2. Provide a short description of the background of the topic and why it is fake news.
3. Attach appropriate graphics. Please provide your source.
4. On the comment section, write a one-sentence explanationof why fake news and misinformation should stop. Please provide your honest opinion.
5. PM me the screenshot of your post.


________________ 1. This is defined as false articles deliberately fabricated to deceive readers, generally with the goal of profiting through clickbait.
________________ 2. It is a content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page.
________________ 3. Who sell social media platform ad space?
________________ 4. What are the wo phases in detecting fake news on social media?
________________ 5. (See question 4)