User:Juliakamin(cs)/research guide/decreasing misinformation

What makes us susceptible - or immune - to fake news

• Amazeen, M. A., & Bucy, E. P. (2019). Conferring Resistance to Digital Disinformation: The Inoculating Influence of Procedural News Knowledge. Journal of Broadcasting & Electronic Media, 63(3), 415–432.

Network modeling

• Wang, W., Ma, Y., Wu, T., Dai, Y., Chen, X., & Braunstein, L. A. (2019). Containing misinformation spreading in temporal social networks.

Tools to correct / innoculate against misinformation

• Amazeen, M. A., Thorson, E., Muddiman, A., & Graves, L. (2018). Correcting Political and Consumer Misperceptions: The Effectiveness and Effects of Rating Scale Versus Contextual Correction Formats. Journalism & Mass Communication Quarterly, 95(1), 28–48. (Summary: Experiment tests effectiveness of "truth scales" and finds that improve capacity of fact-checks to correct misperceptions.)
• Clayton, K., Blair, S., Busam, J. A., Forstner, S., Glance, J., Green, G., … Nyhan, B. (2019). Real Solutions for Fake News? Measuring the Effectiveness of General Warnings and Fact-Check Tags in Reducing Belief in False Stories on Social Media. Political Behavior. (Nutshell: Experiment. Tags that say an article is "disputed" or "rated false" are modestly effective at preventing misperceptions, with "rated false" being more effective."
• De keersmaecker, J., & Roets, A. (2017). ‘Fake news’: Incorrect, but hard to correct. The role of cognitive ability on the impact of false information on social impressions. Intelligence, 65, 107–110.
• Hameleers, M., & van der Meer, T. G. L. A. (2019). Misinformation and Polarization in a High-Choice Media Environment: How Effective Are Political Fact-Checkers? Communication Research, 1–24. (Nutshell: Fact checks can correct misperceptions, but selective exposure make them unlikely to be viewed.)
• Kendeou, P., Walsh, E. K., Smith, E. R., & O’Brien, E. J. (2014). Knowledge Revision Processes in Refutation Texts. Discourse Processes, 51(5–6), 374–397.
• Lewandowsky, S., Ecker, U. K. H., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and Its Correction: Continued Influence and Successful Debiasing. Psychological Science in the Public Interest, 13(3), 106–131.
• Margolin, D. B., Hannak, A., & Weber, I. (2018). Political Fact-Checking on Twitter: When Do Corrections Have an Effect? Political Communication, 35(2), 196–219. (Nutshell: Strong social connections are more effective at correcting misperceptions when sharing fact checker.)
• Mena, Paul. "Cleaning Up Social Media: The Effect of Warning Labels on Likelihood of Sharing False News on Facebook." Policy & Internet (2019). (Flagging stories as false news reduces likelihood they will be shared.)
• Merpert, A., Furman, M., Anauati, M. V., Zommer, L., & Taylor, I. (2018). Is That Even Checkable? An Experimental Study in Identifying Checkable Statements in Political Discourse. (Nutshell: Looks at demographics associated with ability to discern what are "checkable" facts. More interestingly shows that a brief training can improve performance.)
• Nieminen, S., & Rapeli, L. (2018). Fighting misperceptions and doubting journalists’ objectivity: A review of fact-checking literature. Political Studies Review, 1478929918786852. (Reviews effectiveness of fact checking in correcting misperceptions.)
• Nyhan, B., & Reifler, J. (2012). Misinformation and Fact-checking: Research Findings from Social Science. New America Foundation.
• Nyhan, B., & Reifler, J. (2015). Displacing Misinformation about Events: An Experimental Test of Causal Corrections. Journal of Experimental Political Science, 2(1), 81–93. (Nutshell: Tests effectiveness of offering alternative causal explanation for correcting misinformation. Finds it to be more effective than just debunking.)
• Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020). Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychological science, 31(7), 770-780. (Nutshell: Simple accuracy reminder effective in quelling misinformation.)
• Roozenbeek, J., & Linden, S. van der. (2019). Fake news game confers psychological resistance against online misinformation. Palgrave Communications, 5(1). (Nutshell. Develops game in which subjects create fake news using known strategies. Evidence that inoculates users from fake news.)
• Seo, H., Xiong, A., & Lee, D. (2019). Trust It or Not: Effects of Machine-Learning Warnings in Helping Individuals Mitigate Misinformation. In Proceedings of Websci ’19: 11th ACM Conference on Web Science. (Nutshell: Fact checking warnings but not machine learning warnings mitigate misperceptions.)
• Vraga, E. K., & Bode, L. (2017). Using expert sources to correct health misinformation in social media. Science Communication, 39(5), 621-645 (Nutshell: Experiment tests effectiveness of 1 vs 2 sources and use of CDC as corrective to misperceptions. Finds CDC is effective on own.)
• Vraga, E. K., & Bode, L. (2018). I do not believe you: how providing a source corrects health misperceptions across social media platforms. Information, Communication & Society, 21(10), 1337–1353. (Nutshell. Finds source is necessary for correction.)
• Vraga, E. K., Kim, S. C., & Cook, J. (2019). Testing logic-based and humor-based corrections for science, health, and political misinformation on social media. Journal of Broadcasting & Electronic Media, 63(3), 393-414. (Nutshell: Logic-based and humor-based corrections as effective.) (link)
• Walter, N., & Murphy, S. T. (2018). How to unring the bell: A meta-analytic approach to correction of misinformation. Communication Monographs, 85(3), 423–441. (Nutshell: Finds corrections work, though less so with politics and marketing.)
• Wood, T., & Porter, E. (2018). The Elusive Backfire Effect: Mass Attitudes’ Steadfast Factual Adherence. Political Behavior, 32(2), 303–330. (Nutshell: Experiments show there is little risk of "backfiring" effects of corrections.)
• Yeh, M. A., & Jewell, R. D. (2015). The Myth/Fact Message Frame and Persuasion in Advertising: Enhancing Attitudes Toward the Mentally Ill. Journal of Advertising, 44(2), 161–172. (Nutshell: Rhetorical questions can help make debunking more effective.)
• Young, D. G., Jamieson, K. H., Poulsen, S., & Goldring, A. (2018). Fact-checking effectiveness as a function of format and tone: Evaluating FactCheck. org and FlackCheck. org. Journalism & Mass Communication Quarterly, 95(1), 49-75. (Nutshell: Experiment tests effectiveness of humor and video in correcting misperceptions. Finds videos are effective.)

Obstacles to correcting misperceptions

• Jang, S. M., & Kim, J. K. (2018). Third person effects of fake news: Fake news regulation and media literacy interventions. Computers in Human Behavior, 80, 295–302. (Nutshell: We think outparty members are susceptible to fake news, not ourselves.)
• Jun, Y., Meng, R., & Johar, G. V. (2017). Perceived social presence reduces fact-checking. Proceedings of the National Academy of Sciences, 114(23), 5976–5981. (Nutshell: users less likely to fact check in presence of others.)
• Nyhan, B., & Reifler, J. (2010). When Corrections Fail: The Persistence of Political Misperceptions. Political Behavior, 32(2), 303–330. (Nutshell: Corrections can have a backfiring effect.)
• Pennycook, G., Bear, A., Collins, E., & Rand, D. G. (2019). The Implied Truth Effect: Attaching Warnings to a Subset of Fake News Stories Increases Perceived Accuracy of Stories Without Warnings. Social Science Research Network. (Nutshell: while "fake news" tags may be effective of reducing misperceptions on stories that are tagged, they create an "implied truth" risk for other stories.)
• Shin, J., & Thorson, K. (2017). Partisan Selective Sharing: The Biased Diffusion of Fact-Checking Messages on Social Media: Sharing Fact-Checking Messages on Social Media. Journal of Communication, 67(2), 233–255. (Nutshell: partisans only share fact-checks that support their views.)

Tools to decrease the sharing of misinformation

• Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2019). Understanding and reducing the spread of misinformation online (preprint). PsyArXiv. (Nutshell: Suggesting people think about accuracy decreases their propensity to share misinformation.)

Tools to identify misinformation

• Addawood, A., Badawy, A., Lerman, K., & Ferrara, E. (2019). Linguistic Cues to Deception: Identifying Political Trolls on Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 13(01), 15–25.
• Boididou, C., Middleton, S. E., Jin, Z., Papadopoulos, S., Dang-Nguyen, D.-T., Boato, G., & Kompatsiaris, Y. (2018). Verifying information with multimedia content on twitter. Multimedia Tools and Applications, 77(12), 15545–15571. (Nutshell: Compares three tools for identifying fake news.)
• Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016). BotOrNot: A System to Evaluate Social Bots. In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 273–274).
• Epstein, Z., Pennycook, G., & Rand, D. G. (2019). Letting the crowd steer the algorithm: Laypeople can effectively identify misinformation sources. In PsyArXiv Preprints. (Nutshell: Possible to crowdsource reliable sources.)
• Hassan, N., Sultana, A., Wu, Y., Zhang, G., Li, C., Yang, J., & Yu, C. (2014). Data in, Fact out: Automated Monitoring of Facts by FactWatcher. Proceedings of the VLDB Endowment, 7(13), 1557–1560.
• Jin, Z., Cao, J., Zhang, Y., & Luo, J. (2016). News Verification by Exploiting Conflicting Social Viewpoints in Microblogs. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). Phoenix, Arizona: Association for the Advancement of Artificial Intelligence.
• Lim, J., Liu, Z., & Zhou, L. (2019). Detection of Fraudulent Tweets: An Empirical Investigation Using Network Analysis and Deep Learning Technique. IEEE International Conference on Intelligence and Security Informatics.
• Mihaylov, T., Georgiev, G., & Nakov, P. (2015). Finding Opinion Manipulation Trolls in News Community Forums. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning (pp. 310–314). Stroudsburg, PA: Association for Computational Linguistics.
• Paschalides, D., Kornilakis, A., Christodoulou, C., Andreou, R., Pallis, G., Dikaiakos, M. D., & Markatos, E. (2019). Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web.
• Rubin, V., Brogly, C., Conroy, N., Yimin Chen, Cornwell, S., & Asubiaro, T. (2019). A News Verification Browser for the Detection of Clickbait, Satire, and Falsified News. Journal of Open Source Software, 4(35).
• Shao, C., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2016). Hoaxy: A Platform for Tracking Online Misinformation. In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 745–750). Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee.
• Shu, K., Mahudeswaran, D., & Liu, H. (2018). FakeNewsTracker: a tool for fake news collection, detection, and visualization. Computational and Mathematical Organization Theory.
• Zhang, C., Gupta, A., Kauten, C., Deokar, A. V., & Qin, X. (2019). Detecting Fake News for Reducing Misinformation Risks Using Analytics Approaches. European Journal of Operational Research.