Fake News and Mainstream Media Bias
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This SIDLIT 2019 roundtable discussion focused on strategies for verifying and counteracting media bias, fake news, and the various forms of half-truths that are becoming ubiquitous in our national dialog. The discussion will include examples of media bias and alternative facts as well as positive steps that educators and technologists can take to encourage critical thinking as opposed to blind acceptance of news from the plethora of sources now available.
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Fake News and Mainstream Media Bias
- 1. “Fake News” and Mainstream Media Bias
- 2. Errors of Logic: Twisting the Facts Jim Hillen, adjunct instructor Spanish, Johnson County Community College
- 3. Logical Fallacies • Genetic Fallacy • Begging the Claim Circular Argument • Either/Or • Ad hominem • Ad populum • Red Herring • Straw Man • Hasty Generalization • Moral Equivalence • Post hoc ergo propter hoc
- 5. What’s the Solution? “You simply must approach the internet with skepticism. It’s chock full of misinformation, and a lot of it shows up in your email inbox.” [and on Facebook] Leo Notenboom, 18 years programmer at Microsoft, author, blogger and podcaster.
- 6. “Dear fellow citizens, to believe unexamined prose from the Internet is to court folly.”
- 7. Chasing Down Misinformation Dawn Fry, freelance instructional design administrator, C2C secretary, K-BUG education and events coordinater
- 9. Research the Source: University of Michigan Library "Fake News," Lies and Propaganda: How to Sort Fact from Fiction bit.ly/2VFfOsH Photo by Kaboompics .com from Pexels
- 10. Deliberate Misinformation Southern Poverty Law Center list of hate groups www.splcenter.org/hate-map
- 11. Digital Tools Journalists Use to Fact Check Cindy Higgins, instructional designer for State of Kansas, contributor Lawrence Journal World Image compliments of Changefaces.com
- 12. Source Vetter • Trusted News (Google Chrome extension) flags with icon site you’re visiting • Green check: Trustworthy • Red exclamation mark: Not trustworthy • Also marks sites as biased, satire, clickbait, and malicious.
- 13. Site categorizations POLITICAL BIAS: STRONGLY BIASED POLITICAL LANGUAGE. SEXISM: DEMEANING AND ABUSIVE LANGUAGE TARGETED TOWARD A PARTICULAR GENDER, USUALLY WITH STEREOTYPES. RACISM: DEMEANING AND ABUSIVE LANGUAGE TARGETED TOWARD A PARTICULAR ETHNICITY, USUALLY WITH STEREOTYPES. TOXICITY: DEMEANING AND ABUSIVE LANGUAGE IN GENERAL. CLICKBAIT: ARTICLE HEADLINES ARE DESIGNED TO ENTICE READERS INTO CLICKING THE ACCOMPANYING LINK(S).
- 14. Source Vetter • Hoaxy displays how stories from low- credibility sources spread on Twitter. • Often used with Botometer: app assigns score to Twitter users based on likelihood the account is automated to sway opinion. • hoaxy.iuni.iu.edu
- 15. Site Checker • Monitors changes online by accessing archived repositories. • web.archive.org
- 16. Site Checker • Monitors and tracks changes on a specific web page. • You select web page section to monitor. • Get instant alerts when something changes. • distill.io
- 17. Image Vetter: Google Reverse Image Search • Right-click an image in a Google Search, and then Search Google for Images. • Displays similar images and websites that contain these images.
- 18. Image Vetter • Chrome Extension: RevEye Reverse Image Search • To perform inverse image search, right-click any image on web site. • Download at bit.ly/2oGv34T
- 19. Image Vetter • FotoForensics can identify hidden pixels, error level analysis, and metadata details. • fotoforensics.com
- 20. Video Vetter • The Washington Post: Seeing Isn’t Believing • The Fact Checker’s Guide to Manipulated Video • wapo.st/2K81hST
- 21. Video Vetter • InVid checks reliability and accuracy of images and video files. • www.invid-project.eu
- 22. Sign up for weekly email! • Best Digital Tools for Journalism by Ren La Forme • Supported by the American Press Institute and the John S. and James L. Knight Foundation. • Sign up at bit.ly/2MltAQr
- 23. Examination of Trump and His Twitter Feed Ron Rohlf, assistant professor, Informatics, Fort Hays State University
- 40. Thank you for attending! This presentation available online at slideshare.net/jbacon
- 41. Jonathan Bacon Retired director, Educational Technology Center, Johnson County Community College Created with FaceApp
- 42. Latest Concerns: Deep Fakes Seeing is Believing (still true?) The Camera Never Lies (really?) “A lie can travel half way around the world while the truth is putting on its shoes.” ~ Mark Twain
- 43. Deep Fake Videos Use existing footage of subject merged with computer- generated image of person’s face, with dubbed voice for audio
- 45. Photographs are simply a crude statement of fact addressed to the eye. ~ Virginia Woolf “WE BETTER LEARN HOW TO LIVE IN A WORLD WHERE WE TRULY CANNOT BELIEVE OUR EYES.” ~VLOGBROTHERS
- 46. Which Are Real People? None: thispersondoesnotexist.com
- 48. GAN Applications/Issues Valued Uses Imaginary Fashion Models Improve Astronomical Photos Video Game Modeling Reconstruct 3D objects from Photos Construct Voice to Facial Image Problematic Uses Political Propaganda Fake News Fake Friend Requests Pornographic Uses Blackmail
- 49. Deep Fake Video Examples Obama: www.youtube.com/watch?v=cQ54GDm1eL0 (Jordan Peele Impersonation) Kardashian and Zuckerbery: https://www.digitaltrends.com/social-media/kim-kardashian- deepfake-removed-from-youtube/ (Kardashian and Zuckerberg Deepfake videos) More Information: https://www.youtube.com/watch?v=Ex83dhTn0IU (Deepfake Videos Are Getting Real and That’s a Problem | Moving Upstream)
- 50. Inconsistent with previous opinions & behaviors? Influenced by presentation (slick vs. stumbling)? Appeal to emotion vs. reason? Focus on personal attacks, identity politics or straight news? What about your own biases, preconceptions? Possible Defenses and Considerations
- 51. Sources Deep Fakes Video www.youtube.com/watch?v=dMF2i3A9Lzw Deep fake videos of Kardashian, Zuckerberg and Trump news.vice.com/en_us/article/9kxgj3/facebook-refuses-to-remove- deepfakes-of-zuckerberg-trump-and-Kardashian “Mona Lisa brought to life: Samsung AI makes famous painting move and speak” www.pocket- lint.com/gadgets/news/samsung/148153-mona-lisa-brought-to-life- samsung-ai-makes-famous-painting-move-and-speak Protected by First Amendment? uclawreview.org/2019/03/26/deep-fakes-finding-the-balance- between-national-security-and-freedom-of-speech/ GANs www.lyrn.ai/2018/12/26/a-style-based-generator- architecture-for-generative-adversarial-networks/
Editor's Notes
- IFLScience.com recently conducted a similar experiment, publishing an article titled Marijuana Contains “Alien DNA” From Outside Of Our Solar System, NASA Confirms. The article, as of now, has over 141,000 shares, and it isn’t about marijuana or alien DNA at all – it’s an experiment to see how many shares it could attract with an outrageous headline alone. IFLScience states within the post that “We here at IFLS noticed long ago that many of our followers will happily like, share, and offer an opinion on an article – all without ever reading it.” What is IFLScience? PRO-SCIENCE These sources consist of legitimate science or are evidence based through the use of credible scientific sourcing. Legitimate science follows the scientific method, is unbiased and does not use emotional words. These sources also respect the consensus of experts in the given scientific field and strive to publish peer reviewed science. Some sources in this category may have a slight political bias, but adhere to scientific principles. See all Pro-Science sources. Factual Reporting: HIGH Notes: IFL Science is a website that publishes the lighter side of science. A word of caution with IFL Science is they do present some stories with a left of center bias. (12/12/2016) from Media Bias/Fact Check
- “Hear ye my fellow citizens, to believe all prose posted on the Internet is to court folly.”
- Generative adversarial network (GAN): class of machine learning using two neural networks compete with each other. The generative network generates candidates while the discriminative network evaluates them. Contest between the networks: the generative network creates an image of interest, while the discriminative network determines errors vs. true data images. The generative network's training objective is to fool the discriminative network by producing novel candidates that the discriminator thinks are not synthesized (are part of the true data distribution) https://www.lyrn.ai/2018/12/26/a-style-based-generator-architecture-for-generative-adversarial-networks/
- Generative adversarial network (GAN): class of machine learning using two neural networks compete with each other. The generative network generates candidates while the discriminative network evaluates them. Contest between the networks: the generative network creates an image of interest, while the discriminative network determines errors vs. true data images. The generative network's training objective is to fool the discriminative network by producing novel candidates that the discriminator thinks are not synthesized (are part of the true data distribution) https://www.lyrn.ai/2018/12/26/a-style-based-generator-architecture-for-generative-adversarial-networks/