Abstract
This study explores how people perceive the potential utility of trustworthiness indicators and how willing they are to consider contributing to them as a way to combat the problem of misinformation and disinformation on social media. Analysis of qualitative and quantitative data from the survey (N=376) indicates that a majority of respondents believe trustworthiness indicators would be valuable as they can reduce uncertainty and provide guidance on how to interact with content. However, perceptions of how and when these indicators can provide value vary widely in detail. A majority of respondents are also willing to contribute to trustworthiness indicators on social media to some extent due to their sense of duty and personal expertise in information verification practices but are very wary of the effort or burden it would place on them. Respondents who did not want to use or contribute to trustworthiness indicators attributed it to their lack of faith in the concept of trustworthiness indicators stemming from perceived inherent and unsurmountable biases on social media. Together our findings highlight the complexity of designing, structuring and presenting trustworthiness indicators keeping in mind the diverse set of user attitudes and perceptions.
- Maansi Bansal-Travers, David Hammond, Philip Smith, and K. Michael Cummings. 2011. The impact of cigarette pack design, descriptors, and warning labels on risk perception in the U.S. Am J Prev Med 40, 6 (June 2011), 674--682. DOI:https://doi.org/10.1016/j.amepre.2011.01.021Google Scholar
Cross Ref
- Anna E. Bargagliotti and Lingfang (Ivy) Li. 2013. Decision Making Using Rating Systems: When Scale Meets Binary. Decision Sciences 44, 6 (2013), 1121--1137. DOI:https://doi.org/10.1111/deci.12049Google Scholar
Cross Ref
- Bill Bishop. Americans have lost faith in institutions. That's not because of Trump or 'fake news.' Washington Post. Retrieved May 12, 2020 from https://www.washingtonpost.com/posteverything/wp/2017/03/03/americans-have-lost-faith-in-institutions-thats-not-because-of-trump-or-fake-news/Google Scholar
- Leticia Bode and Emily K. Vraga. 2015. In Related News, That Was Wrong: The Correction of Misinformation Through Related Stories Functionality in Social Media. JOURNAL OF COMMUNICATION 65, 4 (August 2015), 619--638. DOI:https://doi.org/10.1111/jcom.12166Google Scholar
- Shannon Bond. 2020. Twitter Expands Warning Labels To Slow Spread of Election Misinformation?: NPR. Retrieved October 24, 2020 from https://www.npr.org/2020/10/09/922028482/twitter-expands-warning-labels-to-slow-spread-of-election-misinformationGoogle Scholar
- Manuel Cabrera, Leandro Machín, Alejandra Arrúa, Lucía Antúnez, María Rosa Curutchet, Ana Giménez, and Gastón Ares. 2017. Nutrition warnings as front-of-pack labels: influence of design features on healthfulness perception and attentional capture. Public Health Nutr 20, 18 (December 2017), 3360--3371. DOI:https://doi.org/10.1017/S136898001700249XGoogle Scholar
Cross Ref
- Katherine Clayton, Spencer Blair, Jonathan A. Busam, Samuel Forstner, John Glance, Guy Green, Anna Kawata, Akhila Kovvuri, Jonathan Martin, Evan Morgan, Morgan Sandhu, Rachel Sang, Rachel Scholz-Bright, Austin T. Welch, Andrew G. Wolff, Amanda Zhou, and Brendan Nyhan. 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. Polit Behav (February 2019). DOI:https://doi.org/10.1007/s11109-019-09533-0Google Scholar
- Juliet Corbin and Anselm Strauss. 2007. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (3rd edition ed.). SAGE Publications, Inc, Los Angeles, Calif.Google Scholar
- Dan Cosley, Shyong K. Lam, Istvan Albert, Joseph A. Konstan, and John Riedl. 2003. Is seeing believing? how recommender system interfaces affect users' opinions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '03), Association for Computing Machinery, Ft. Lauderdale, Florida, USA, 585--592. DOI:https://doi.org/10.1145/642611.642713 Google Scholar
Digital Library
- Ullrich K. H. Ecker, Stephan Lewandowsky, and David T. W. Tang. 2010. Explicit warnings reduce but do not eliminate the continued influence of misinformation. Mem Cognit 38, 8 (December 2010), 1087--1100. DOI:https://doi.org/10.3758/MC.38.8.1087Google Scholar
Cross Ref
- Mingkun Gao, Ziang Xiao, Karrie Karahalios, and Wai-Tat Fu. 2018. To Label or Not to Label: The Effect of Stance and Credibility Labels on Readers' Selection and Perception of News Articles. Proc. ACM Hum.-Comput. Interact. 2, CSCW (November 2018), 55:1--55:16. DOI:https://doi.org/10.1145/3274324 Google Scholar
Digital Library
- Andrew M. Guess, Brendan Nyhan, and Jason Reifler. 2020. Exposure to untrustworthy websites in the 2016 US election. Nat Hum Behav (March 2020), 1--9. DOI:https://doi.org/10.1038/s41562-020-0833-xGoogle Scholar
- Hendrik Heuer and Andreas Breiter. 2018. Trust in news on social media. In Proceedings of the 10th Nordic Conference on Human-Computer Interaction (NordiCHI '18), Association for Computing Machinery, Oslo, Norway, 137--147. DOI:https://doi.org/10.1145/3240167.3240172 Google Scholar
Digital Library
- Avery E. Holton, Mark Coddington, and Homero Gil de Zúñiga. 2013. Whose News? Whose Values? Journalism Practice 7, 6 (December 2013), 720--737. DOI:https://doi.org/10.1080/17512786.2013.766062Google Scholar
- Nabil Jeddi and Imed Zaiem. 2010. The Impact of Label Perception on the Consumer's Purchase Intention: An application on food products. IBIMA Business Review (January 2010).Google Scholar
- Shan Jiang and Christo Wilson. 2018. Linguistic Signals under Misinformation and Fact-Checking: Evidence from User Comments on Social Media. Proc. ACM Hum.-Comput. Interact. 2, CSCW (November 2018), 82:1--82:23. DOI:https://doi.org/10.1145/3274351 Google Scholar
Digital Library
- Michael Kranish. 2014. Facebook draws fire on ?related articles' push - The Boston Globe. The Boston Globe. Retrieved May 1, 2020 from https://www.bostonglobe.com/news/nation/2014/05/03/facebook-push-related-articles-users-without-checking-credibility-draws-fire/rPae4M2LlzpVHIJAmfDYNL/story.htmlGoogle Scholar
- Travis Kriplean, Jonathan Morgan, Deen Freelon, Alan Borning, and Lance Bennett. 2012. Supporting reflective public thought with considerit. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW '12), Association for Computing Machinery, Seattle, Washington, USA, 265--274. DOI:https://doi.org/10.1145/2145204.2145249 Google Scholar
Digital Library
- Issie Lapowsky. Gallup Poll: Labeling Sites May Help Stop Fake News Spread. Wired. Retrieved May 12, 2020 from https://www.wired.com/story/gallup-poll-fake-news-ratings/Google Scholar
- Alex Leavitt and John J. Robinson. 2017. Upvote My News: The Practices of Peer Information Aggregation for Breaking News on reddit.com. Proc. ACM Hum.-Comput. Interact. 1, CSCW (December 2017), 65:1--65:18. DOI:https://doi.org/10.1145/3134700 Google Scholar
Digital Library
- Rachel Lerman. Facebook says it has taken down 7 million posts for spreading coronavirus misinformation. Washington Post. Retrieved October 29, 2020 from https://www.washingtonpost.com/technology/2020/08/11/facebook-covid-misinformation-takedowns/Google Scholar
- Stephan Lewandowsky, Ullrich K. H. Ecker, Colleen M. Seifert, Norbert Schwarz, and John Cook. 2012. Misinformation and Its Correction: Continued Influence and Successful Debiasing. Psychol Sci Public Interest 13, 3 (December 2012), 106--131. DOI:https://doi.org/10.1177/1529100612451018Google Scholar
Cross Ref
- Tessa Lyons. 2017. Replacing Disputed Flags With Related Articles. Facebook News. Retrieved May 12, 2020 from https://about.fb.com/news/2017/12/news-feed-fyi-updates-in-our-fight-against-misinformation/Google Scholar
- Warih Maharani, Dwi H. Widyantoro, and Masayu L. Khodra. 2016. Discovering Users' Perceptions on Rating Visualizations. In Proceedings of the 2nd International Conference in HCI and UX Indonesia 2016 (CHIuXiD '16), Association for Computing Machinery, Jakarta, Indonesia, 31--38. DOI:https://doi.org/10.1145/2898459.2898464 Google Scholar
Digital Library
- Patricia Moravec, Randall Minas, and Alan Dennis. 2019. Fake News on Social Media: People Believe What They Want to Believe When it Makes No Sense At All. MIS Quarterly 43, 4 (2019), 1343--1360. DOI:https://doi.org/10.25300/MISQ/2019/15505Google Scholar
- Adam Mosseri. 2016. Addressing Hoaxes and Fake News. Facebook News. Retrieved May 12, 2020 from https://about.fb.com/news/2016/12/news-feed-fyi-addressing-hoaxes-and-fake-news/Google Scholar
- Nic Newman, Richard Fletcher, Antonis Kalogeropoulos, David A.L. Levy, and Rasmus Kleis Neilsen. 2017. Digital News Report 2017. Reuters Institute for the Study of Journalism. Retrieved May 11, 2020 from https://reutersinstitute.politics.ox.ac.uk/sites/default/files/Digital%20News%20Report%202017%20web_0.pdfGoogle Scholar
- Rasmus Kleis Nielsen and Lucas Graves. 2017. "News you don't believe": Audience perspectives on fake news. Reuters Institute for the Study of Journalism. Retrieved May 11, 2020 from https://reutersinstitute.politics.ox.ac.uk/our-research/news-you-dont-believe-audience-perspectives-fake-newsGoogle Scholar
- Brendan Nyhan and Jason Reifler. 2010. When Corrections Fail: The Persistence of Political Misperceptions. Polit Behav 32, 2 (June 2010), 303--330. DOI:https://doi.org/10.1007/s11109-010--9112--2Google Scholar
Cross Ref
- Denise-Marie Ordway, Journalist's Resource September 1, and 2017. 2017. Fake news and the spread of misinformation: A research roundup. Journalist's Resource. Retrieved May 1, 2020 from https://journalistsresource.org/studies/society/internet/fake-news-conspiracy-theories-journalism-research/Google Scholar
- Will Oremus. 2019. These Startups Want to Protect You From Fake News. Can You Trust Them? Slate Magazine. Retrieved May 12, 2020 from https://slate.com/technology/2019/01/newsguard-nuzzelrank-media-ratings-fake-news.htmlGoogle Scholar
- Gordon Pennycook, Adam Bear, and Evan Collins. 2019. The Implied Truth Effect: Attaching Warnings to a Subset of Fake News Headlines Increases Perceived Accuracy of Headlines Without Warnings. Management Science (August 2019). DOI:https://doi.org/10.1287/mnsc.2019.3478Google Scholar
- Gordon Pennycook, Tyrone Cannon, and David G. Rand. 2018. Prior Exposure Increases Perceived Accuracy of Fake News. Social Science Research Network, Rochester, NY. DOI:https://doi.org/10.2139/ssrn.2958246Google Scholar
- Sarah Perez. 2020. Twitter to add labels and warning messages to disputed and misleading COVID-19 info. TechCrunch. Retrieved October 24, 2020 from https://social.techcrunch.com/2020/05/11/twitter-to-add-labels-and-warning-messages-to-disputed-and-misleading-covid-19-info/Google Scholar
- Yoel Roth and Nick Pickles. 2020. Updating our approach to misleading information. Retrieved October 24, 2020 from https://blog.twitter.com/en_us/topics/product/2020/updating-our-approach-to-misleading-information.htmlGoogle Scholar
- Jonathon P. Schuldt. 2013. Does Green Mean Healthy? Nutrition Label Color Affects Perceptions of Healthfulness. Health Communication 28, 8 (November 2013), 814--821. DOI:https://doi.org/10.1080/10410236.2012.725270Google Scholar
Cross Ref
- Haeseung Seo, Aiping Xiong, and Dongwon Lee. 2019. Trust It or Not: Effects of Machine-Learning Warnings in Helping Individuals Mitigate Misinformation. In Proceedings of the 10th ACM Conference on Web Science (WebSci '19), Association for Computing Machinery, Boston, Massachusetts, USA, 265--274. DOI:https://doi.org/10.1145/3292522.3326012 Google Scholar
Digital Library
- Ricky J. Sethi. 2017. Crowdsourcing the Verification of Fake News and Alternative Facts. In Proceedings of the 28th ACM Conference on Hypertext and Social Media (HT '17), Association for Computing Machinery, Prague, Czech Republic, 315--316. DOI:https://doi.org/10.1145/3078714.3078746 Google Scholar
Digital Library
- Chengcheng Shao, Giovanni Luca Ciampaglia, Alessandro Flammini, and Filippo Menczer. 2016. Hoaxy: A Platform for Tracking Online Misinformation. In Proceedings of the 25th International Conference Companion on World Wide Web (WWW '16 Companion), International World Wide Web Conferences Steering Committee, Montréal, Québec, Canada, 745--750. DOI:https://doi.org/10.1145/2872518.2890098 Google Scholar
Digital Library
- Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake News Detection on Social Media: A Data Mining Perspective. SIGKDD Explor. Newsl. 19, 1 (September 2017), 22--36. DOI:https://doi.org/10.1145/3137597.3137600 Google Scholar
Digital Library
- E. Isaac Sparling and Shilad Sen. 2011. Rating: how difficult is it? In Proceedings of the fifth ACM conference on Recommender systems (RecSys '11), Association for Computing Machinery, Chicago, Illinois, USA, 149--156. DOI:https://doi.org/10.1145/2043932.2043961 Google Scholar
Digital Library
- Kate Starbird, Ahmer Arif, and Tom Wilson. 2019. Disinformation as Collaborative Work: Surfacing the Participatory Nature of Strategic Information Operations. Proc. ACM Hum.-Comput. Interact. 3, CSCW (November 2019), 127:1--127:26. DOI:https://doi.org/10.1145/3359229 Google Scholar
Digital Library
- Brian Stelter. 2016. Facebook to start putting warning labels on "fake news." Retrieved October 24, 2020 from https://money.cnn.com/2016/12/15/media/facebook-fake-news-warning-labels/Google Scholar
- Briony Swire-Thompson, Joseph DeGutis, and David Lazer. 2020. Searching for the Backfire Effect: Measurement and Design Considerations. J Appl Res Mem Cogn 9, 3 (September 2020), 286--299. DOI:https://doi.org/10.1016/j.jarmac.2020.06.006Google Scholar
Cross Ref
- Sebastian Tschiatschek, Adish Singla, Manuel Gomez Rodriguez, Arpit Merchant, and Andreas Krause. 2018. Fake News Detection in Social Networks via Crowd Signals. In Companion Proceedings of the The Web Conference 2018 (WWW '18), International World Wide Web Conferences Steering Committee, Lyon, France, 517--524. DOI:https://doi.org/10.1145/3184558.3188722 Google Scholar
Digital Library
- Georgia Wells. 2020. Twitter to Add Labels to Disputed Coronavirus Posts, as Misinformation Proliferates. Wall Street Journal. Retrieved May 16, 2020 from https://www.wsj.com/articles/twitter-to-add-labels-to-disputed-coronavirus-posts-as-misinformation-proliferates-11589226911Google Scholar
- Georgia Wells and Lukas I. Alpert. 2018. In Facebook's Effort to Fight Fake News, Human Fact-Checkers Struggle to Keep Up. Wall Street Journal. Retrieved October 29, 2020 from https://www.wsj.com/articles/in-facebooks-effort-to-fight-fake-news-human-fact-checkers-play-a-supporting-role-1539856800Google Scholar
- Darrell M. West. 2017. How to combat fake news and disinformation. Retrieved May 11, 2020 from https://www.brookings.edu/research/how-to-combat-fake-news-and-disinformation/Google Scholar
- Tom Wilson, Kaitlyn Zhou, and Kate Starbird. 2018. Assembling Strategic Narratives: Information Operations as Collaborative Work within an Online Community. Proc. ACM Hum.-Comput. Interact. 2, CSCW (November 2018), 183:1--183:26. DOI:https://doi.org/10.1145/3274452 Google Scholar
Digital Library
- Chen Xu and Qin Zhang. 2019. The dominant factor of social tags for users' decision behavior on e-commerce websites: Color or text. Journal of the Association for Information Science and Technology 70, 9 (2019), 942--953. DOI:https://doi.org/10.1002/asi.24118Google Scholar
Digital Library
- 2018. A multi-dimensional approach to disinformation: report of the independent High-level Group on fake news and online disinformation. Publication Office of European Union. Retrieved May 11, 2020 from http://op.europa.eu/en/publication-detail/-/publication/6ef4df8b-4cea-11e8-be1d-01aa75ed71a1Google Scholar
- How Facebook's Fact-Checking Program Works. How Facebook's Fact-Checking Program Works. Retrieved October 24, 2020 from https://www.facebook.com/journalismproject/programs/third-party-fact-checking/how-it-worksGoogle Scholar
Index Terms
By the Crowd and for the Crowd: Perceived Utility and Willingness to Contribute to Trustworthiness Indicators on Social Media
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