Protection from 'Fake News': The Need for Descriptive Factual Labeling for Online Content

So-called ‘fake news’—deceptive online content that attempts to manipulate readers—is a growing problem. A tool of intelligence agencies, scammers and marketers alike, it has been blamed for election interference, public confusion and other issues in the United States and beyond. This problem is made particularly pronounced as younger generations choose social media sources over journalistic sources for their information. This paper considers the prospective solution of providing consumers with ‘nutrition facts’-style information for online content. To this end, it reviews prior work in product labeling and considers several possible approaches and the arguments for and against such labels. Based on this analysis, a case is made for the need for a nutrition facts-based labeling scheme for online content.

[1]  Victoria L. Rubin,et al.  Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News , 2016 .

[2]  Eunsol Choi,et al.  Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking , 2017, EMNLP.

[3]  Jeremy Straub,et al.  Introducing & Evaluating ‘Nutrition Facts’ for Online Content , 2020, 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security).

[4]  Lyndon N. Smith,et al.  The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions , 2021, Comput. Ind..

[5]  Neha Gupta,et al.  Falling for Fake News: Investigating the Consumption of News via Social Media , 2018, CHI.

[6]  Ursula Smartt,et al.  Social media and fake news , 2020 .

[7]  Daniel M. Smith Election , 2018, Dynasties and Democracy.

[8]  William Yang Wang “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection , 2017, ACL.

[9]  Fern Wickson,et al.  No scientific consensus on GMO safety , 2015, Environmental Sciences Europe.

[10]  Jeremy Straub,et al.  Evaluation of algorithms for fake news identification , 2019, Defense + Commercial Sensing.

[11]  Jayashree Padmanabhan,et al.  Machine Learning in Automatic Speech Recognition: A Survey , 2015 .

[12]  Fenglong Ma,et al.  Weak Supervision for Fake News Detection via Reinforcement Learning , 2019, AAAI.

[13]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[14]  G. Kauwell,et al.  The New Nutrition Facts Label , 2018 .

[15]  Susan Wiedenbeck,et al.  Measuring Online Trust of Websites: Credibility, Perceived Ease of Use, and Risk , 2005, AMCIS.

[16]  Ahmet Aker,et al.  Information Nutrition Labels: A Plugin for Online News Evaluation , 2018 .

[17]  Eugenio Tacchini,et al.  Some Like it Hoax: Automated Fake News Detection in Social Networks , 2017, ArXiv.

[18]  Josiane Mothe,et al.  Information Nutritional Label and Word Embedding to Estimate Information Check-Worthiness , 2019, SIGIR.

[19]  R. Brownson,et al.  Introduction: Fake News, Science, and the Growing Multiplicity and Duplicity of Information Sources. , 2020, Annual Review of Public Health.

[20]  Sung-Un Yang,et al.  Measuring Social Media Credibility: A Study on a Measure of Blog Credibility , 2011 .

[21]  Meital Balmas,et al.  When Fake News Becomes Real , 2014, Commun. Res..

[22]  Sibel Adali,et al.  This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News , 2017, Proceedings of the International AAAI Conference on Web and Social Media.

[23]  Mykhailo Granik,et al.  Fake news detection using naive Bayes classifier , 2017, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON).

[24]  D. Lazer,et al.  Fake news on Twitter during the 2016 U.S. presidential election , 2019, Science.

[25]  C. Gaziano,et al.  Measuring the Concept of Credibility , 1986 .

[26]  Edson C. Tandoc,et al.  Defining “Fake News” , 2018 .

[27]  V. Bakir,et al.  Fake News and The Economy of Emotions , 2018 .

[28]  A. Cooper,et al.  A short review on susceptibility to falling for fake political news. , 2020, Current opinion in psychology.

[29]  Philip Meyer,et al.  Defining and Measuring Credibility of Newspapers: Developing an Index , 1988 .

[30]  Yuan He,et al.  Fake news or bad news? Toward an emotion-driven cognitive dissonance model of misinformation diffusion , 2020 .

[31]  Miriam J. Metzger,et al.  The science of fake news , 2018, Science.

[32]  M. Gershwin,et al.  The history and contemporary challenges of the US Food and Drug Administration. , 2007, Clinical therapeutics.

[33]  Marisa Murray,et al.  Screen time is associated with depression and anxiety in Canadian youth. , 2015, Preventive medicine.

[34]  Jeremy Straub,et al.  Development of a 'fake news' machine learning classifier and a dataset for its testing , 2019, Defense + Commercial Sensing.

[35]  Andrea Guazzini,et al.  Reviewing Stranger on the Internet: The Role of Identifiability through "Reputation" in Online Decision Making , 2021, Future Internet.

[36]  S. van der Linden,et al.  The fake news game: actively inoculating against the risk of misinformation , 2019 .

[37]  I. Barkan Industry invites regulation: the passage of the Pure Food and Drug Act of 1906. , 1985, American journal of public health.

[38]  L. Miller,et al.  Making healthy food choices using nutrition facts panels. The roles of knowledge, motivation, dietary modifications goals, and age , 2012, Appetite.

[39]  Jeffrey O'Holleran Blood Code: The History and Future of Video Game Censorship , 2010, J. Telecommun. High Technol. Law.

[40]  P. Mena Cleaning Up Social Media: The Effect of Warning Labels on Likelihood of Sharing False News on Facebook , 2020, Policy & Internet.

[41]  S. Domingues-Montanari Clinical and psychological effects of excessive screen time on children , 2017, Journal of paediatrics and child health.

[42]  Aythami Morales,et al.  DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection , 2020, Inf. Fusion.

[43]  David G. Rand,et al.  Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning , 2019, Cognition.

[44]  M. Gentzkow,et al.  Social Media and Fake News in the 2016 Election , 2017 .

[45]  Benno Stein,et al.  An Information Nutritional Label for Online Documents , 2018, SIGIR Forum.

[46]  Joshua A. Braun,et al.  Fake News, Real Money: Ad Tech Platforms, Profit-Driven Hoaxes, and the Business of Journalism , 2019, Digital Journalism.

[47]  James Fairbanks,et al.  Credibility Assessment in the News : Do we need to read ? , 2018 .

[48]  David G. Rand,et al.  Prior Exposure Increases Perceived Accuracy of Fake News , 2018, Journal of experimental psychology. General.

[49]  Regina M. Marchi With Facebook, Blogs, and Fake News, Teens Reject Journalistic “Objectivity” , 2012 .

[50]  Sungyong Seo,et al.  CSI: A Hybrid Deep Model for Fake News Detection , 2017, CIKM.

[51]  David G Schlundt,et al.  Literacy, numeracy, and portion-size estimation skills. , 2009, American journal of preventive medicine.