research-article

Reliability Criteria for News Websites

Authors:
Hendrik Heuer

Harvard University, USA and University of Bremen, Germany

Harvard University, USA and University of Bremen, Germany

0000-0003-1919-9016
View Profile

,
Elena L. Glassman

Harvard University, USA

Harvard University, USA

0000-0001-5178-3496
View Profile

Authors Info & Claims

ACM Transactions on Computer-Human Interaction Volume 31 Issue 2Article No.: 21pp 1–33https://doi.org/10.1145/3635147

Published:29 January 2024Publication History

ACM Transactions on Computer-Human Interaction

Abstract

Misinformation poses a threat to democracy and to people’s health. Reliability criteria for news websites can help people identify misinformation. But despite their importance, there has been no empirically substantiated list of criteria for distinguishing reliable from unreliable news websites. We identify reliability criteria, describe how they are applied in practice, and compare them to prior work. Based on our analysis, we distinguish between manipulable and less manipulable criteria and compare politically diverse laypeople as end-users and journalists as expert users. We discuss 11 widely recognized criteria, including the following 6 criteria that are difficult to manipulate: content, political alignment, authors, professional standards, what sources are used, and a website’s reputation. Finally, we describe how technology may be able to support people in applying these criteria in practice to assess the reliability of websites.

REFERENCES

[1] Aldrich John H., Sullivan John L., and Borgida Eugene. 1989. Foreign affairs and issue voting: Do presidential candidates “waltz before a blind audience?” Am. Polit. Sci. Rev. 83, 1 (1989), 123–141.Google ScholarCross Ref
[2] Allen Jennifer, Martel Cameron, and Rand David G.. 2022. Birds of a feather don’t fact-check each other: Partisanship and the evaluation of news in Twitter’s birdwatch crowdsourced fact-checking program. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’22). Association for Computing Machinery, New York, NY, Article 245, 19 pages. DOI:Google ScholarDigital Library
[3] Altay Sacha, Berriche Manon, Heuer Hendrik, Farkas Johan, and Rathje Steven. 2023. A survey of expert views on misinformation: Definitions, determinants, solutions, and future of the field. Harv. Kenn. School Misinf. Rev. 4, 4 (2023).Google Scholar
[4] Altay Sacha, Araujo Emma de, and Mercier Hugo. 2021. “If this account is true, it is most enormously wonderful”: Interestingness-if-true and the sharing of true and false news. Digit. Journal. 0, 0 (2021), 1–22. DOI:Google ScholarCross Ref
[5] Arechar Antonio Alonso, Allen Jennifer Nancy Lee, Cole Rocky, Epstein Ziv, Garimella Kiran, Gully Andrew, Lu Jackson G., Ross Robert M., Stagnaro Michael, Zhang Jerry, Gordon Pennycook, and David Rand. 2023. Understanding and reducing online misinformation across 16 countries on six continents. Nature Human Behaviour 7, 9 (2023), 1502--1513.Google Scholar
[6] Asr Fatemeh Torabi and Taboada Maite. 2019. Big data and quality data for fake news and misinformation detection. Big Data Societ. 6, 1 (2019). DOI:Google ScholarCross Ref
[7] Bhuiyan Md Momen, Zhang Amy X., Sehat Connie Moon, and Mitra Tanushree. 2020. Investigating differences in crowdsourced news credibility assessment: Raters, tasks, and expert criteria. Proc. ACM Hum.-comput. Interact. 4, CSCW2, Article 93 (Oct. 2020), 26 pages. DOI:Google ScholarDigital Library
[8] Boyd Danah. 2018. You Think You Want Media Literacy...Do You? Retrieved from https://points.datasociety.net/you-think-you-want-media-literacy-do-you-7cad6af18ec2Google Scholar
[9] Bradshaw Samantha, Howard Philip N., Kollanyi Bence, and Neudert Lisa-Maria. 2020. Sourcing and automation of political news and information over social media in the United States, 2016–2018. Polit. Commun. 37, 2 (2020), 173–193.Google ScholarCross Ref
[10] Braun Virginia and Clarke Victoria. 2006. Using thematic analysis in psychology. Qualitat. Res. Psychol. 3, 2 (Jan. 2006), 77–101. DOI:Google ScholarCross Ref
[11] Braun Virginia and Clarke Victoria. 2019. Reflecting on reflexive thematic analysis. Qualitat. Res. Sport, Exerc. Health 11, 4 (2019), 589–597. DOI:Google ScholarCross Ref
[12] Castillo Carlos, Mendoza Marcelo, and Poblete Barbara. 2011. Information credibility on Twitter. In Proceedings of the 20th International Conference on World Wide Web (WWW’11). Association for Computing Machinery, New York, NY, 675–684. DOI:Google ScholarDigital Library
[13] Chołoniewski Jan, Sienkiewicz Julian, Dretnik Naum, Leban Gregor, Thelwall Mike, and Hołyst Janusz A.. 2020. A calibrated measure to compare fluctuations of different entities across timescales. Scient. Rep. 10, 1 (2020), 1–16.Google Scholar
[14] Corbin Juliet and Strauss Anselm. 2014. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Sage Publications.Google Scholar
[15] Carpini M. X. Delli and Keeter S.. 1997. What Americans Know About Politics and Why It Matters. Yale University Press, New Haven.Google Scholar
[16] M. Dimock, C. Doherty, J. Kiley, and V. Krishnamurthy. 2014. Beyond red vs. blue: The political typology. Pew Research Center. https://www.pewresearch.org/politics/2021/11/09/beyond-red-vs-blue-the-political-typology-2/Google Scholar
[17] Donath Judith. 2007. Signals, cues and meaning. Signals, Truth Des. (2007). https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=e32511687c93ba76c4db1408f4a6ad4f277dc397Google Scholar
[18] Donath Judith. 2007. Signals in social supernets. J. Comput.-Mediat. Commun. 13, 1 (10 2007), 231–251. DOI:Google ScholarDigital Library
[19] Erikson Josefina and Josefsson Cecilia. 2019. Does higher education matter for MPs in their parliamentary work? Evidence from the Swedish parliament. Representation 55, 1 (2019), 65–80.Google ScholarCross Ref
[20] Faragó Laura, Kende Anna, and Krekó Péter. 2020. We only believe in news that we doctored ourselves. Soc. Psychol. 51, 2 (2020).Google ScholarCross Ref
[21] Flintham Martin, Karner Christian, Bachour Khaled, Creswick Helen, Gupta Neha, and Moran Stuart. 2018. Falling for fake news: Investigating the consumption of news via social media. In Proceedings of the CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, 1–10. DOI:Google ScholarDigital Library
[22] Fogg B. J., Marshall Jonathan, Laraki Othman, Osipovich Alex, Varma Chris, Fang Nicholas, Paul Jyoti, Rangnekar Akshay, Shon John, Swani Preeti, and Treinen Marissa. 2001. What makes web sites credible? A report on a large quantitative study. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’01). Association for Computing Machinery, New York, NY, 61–68. DOI:Google ScholarDigital Library
[23] Fogg B. J., Soohoo Cathy, Danielson David R., Marable Leslie, Stanford Julianne, and Tauber Ellen R.. 2003. How do users evaluate the credibility of web sites? A study with over 2,500 participants. In Proceedings of the Conference on Designing for User Experiences (DUX’03). Association for Computing Machinery, New York, NY, 1–15. DOI:Google ScholarDigital Library
[24] Gruppi Maurício, Horne Benjamin D., and Adalı Sibel. 2020. NELA-GT-2019: A Large Multi-labelled News Dataset for the Study of Misinformation in News Articles. arxiv:2003.08444Google Scholar
[25] Guess Andrew M., Lerner Michael, Lyons Benjamin, Montgomery Jacob M., Nyhan Brendan, Reifler Jason, and Sircar Neelanjan. 2020. A digital media literacy intervention increases discernment between mainstream and false news in the United States and India. Proc. Nat’l Acad. Sci. 117, 27 (2020), 15536–15545. DOI:Google ScholarCross Ref
[26] Heuer Hendrik and Glassman Elena Leah. 2022. A comparative evaluation of interventions against misinformation: Augmenting the WHO checklist. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’22). Association for Computing Machinery, New York, NY, Article 241, 21 pages. DOI:Google ScholarDigital Library
[27] Heuer Hendrik, Jarke Juliane, and Breiter Andreas. 2021. Machine learning in tutorials—Universal applicability, underinformed application, and other misconceptions. Big Data Societ. 8, 1 (2021). DOI:Google ScholarCross Ref
[28] Humprecht Edda, Esser Frank, and Aelst Peter Van. 2020. Resilience to online disinformation: A framework for cross-national comparative research. Int. J. Press/Polit. 25, 3 (2020), 493–516. DOI:Google ScholarCross Ref
[29] Jahanbakhsh Farnaz, Zhang Amy X., Berinsky Adam J., Pennycook Gordon, Rand David G., and Karger David R.. 2021. Exploring lightweight interventions at posting time to reduce the sharing of misinformation on social media. Proc. ACM Hum.-comput. Interact. 5, CSCW1, Article 18 (Apr. 2021), 42 pages. DOI:Google ScholarDigital Library
[30] Jiang Shan and Wilson Christo. 2018. Linguistic signals under misinformation and fact-checking: Evidence from user comments on social media. Proc. ACM Hum.-comput. Interact. 2, CSCW, Article 82 (Nov. 2018), 23 pages. DOI:Google ScholarDigital Library
[31] Kahan Dan M.. 2013. Ideology, motivated reasoning, and cognitive reflection. Judg. Decis. Mak. 8, 4 (2013), 407–424.Google ScholarCross Ref
[32] Kirchner Jan and Reuter Christian. 2020. Countering fake news: A comparison of possible solutions regarding user acceptance and effectiveness. Proc. ACM Hum.-comput. Interact. 4, CSCW2, Article 140 (Oct. 2020), 27 pages. DOI:Google ScholarDigital Library
[33] Kuniavsky Mike. 2003. Observing the User Experience: A Practitioner’s Guide to User Research. Elsevier.Google ScholarDigital Library
[34] Kuusela Hannu and Paul Pallab. 2000. A comparison of concurrent and retrospective verbal protocol analysis. Amer. J. Psychol. 113, 3 (2000), 387–404.Google ScholarCross Ref
[35] Lampinen Airi. 2015. Deceptively simple: Unpacking the notion of “Sharing.” Social Media + Societ. 1, 1 (2015), 2056305115578135. DOI:Google ScholarCross Ref
[36] Lazer David M. J., Baum Matthew A., Benkler Yochai, Berinsky Adam J., Greenhill Kelly M., Menczer Filippo, Metzger Miriam J., Nyhan Brendan, Pennycook Gordon, Rothschild David, Michael Schudson, Steven A. Sloman, Cass R. Sunstein, Emily A. Thorson, Duncan J. Watts, and Jonathan L. Zittrain. 2018. The science of fake news. Science 359, 6380 (2018), 1094–1096.Google ScholarCross Ref
[37] Lewandowsky Stephan, Ecker Ullrich K. H., Seifert Colleen M., Schwarz Norbert, and Cook John. 2012. Misinformation and its correction: Continued influence and successful debiasing. Psychol. Sci. Pub. Interest 13, 3 (2012), 106–131. DOI:Google ScholarCross Ref
[38] P. Mayring. 2000. Qualitative Content Analysis. Forum Qualitative Sozialforschung Forum: Qualitative Social Research 1, 2 (2000). Google ScholarCross Ref
[39] Messing Solomon, DeGregorio Christina, Hillenbrand Bennett, King Gary, Mahanti Saurav, Mukerjee Zagreb, Nayak Chaya, Persily Nate, State Bogdan, and Wilkins Arjun. 2020. Facebook Privacy-protected Full URLs Data Set. DOI:Google ScholarCross Ref
[40] Mitchell Amy, Gottfried Jeffrey, Barthel Michael, and Sumida Nami. 2018. Can Americans tell factual from opinion statements in the news. Pew Res. Cent. Journal. Proj. (2018). Retrieved from https://www.journalism.org/2018/06/18/distinguishing-between-factual-and-opinion-statements-in-the-newsGoogle Scholar
[41] Mitchell Amy, Gottfried Jeffrey, Stocking Galen, Walker Mason, and Fedeli Sophia. 2019. Many Americans Say Made-Up News Is a Critical Problem that Needs to Be Fixed. Retrieved from https://www.journalism.org/2019/06/05/many-americans-say-made-up-news-is-a-critical-problem-that-needs-to-be-fixed/Google Scholar
[42] Newman Nic. 2020. Overview and Key Findings of the 2020 Digital News Report. Retrieved from https://www.digitalnewsreport.org/survey/2020/overview-key-findings-2020/Google Scholar
[43] Newman Nic, Fletcher Richard, Robertson Craig T., Eddy Kirsten, and Nielsen Rasmus Kleis. 2022. Reuters institute digital news report 2022. Retrieved from https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2022Google Scholar
[44] NewsGuard. 2022. Rating process and criteria. Retrieved from https://www.newsguardtech.com/ratings/rating-process-criteria/Google Scholar
[45] Painter David Lynn and Fernandes Juliana. 2022. “The big lie”: How fact checking influences support for insurrection. Am. Behav. Scient. (2022). Google ScholarCross Ref
[46] Pasquetto Irene V., Swire-Thompson Briony, Amazeen Michelle A., Benevenuto Fabrício, Brashier Nadia M., Bond Robert M., Bozarth Lia C., Budak Ceren, Ecker Ullrich K. H., Fazio Lisa K., Emilio Ferrara, Andrew J. Flanagin, Alessandro Flammini, Deen Freelon, Nir Grinberg, Ralph Hertwig, Kathleen Hall Jamieson, Kenneth Joseph, Jason J. Jones, R. Kelly Garrett, Daniel Kreiss, Shannon McGregor, Jasmine McNealy, Drew Margolin, Alice Marwick, FiIippo Menczer, Miriam J. Metzger, Seungahn Nah, Stephan Lewandowsky, Philipp Lorenz-Spreen, Pablo Ortellado, Gordon Pennycook, Ethan Porter, David G. Rand, Ronald E. Robertson, Francesca Tripodi, Soroush Vosoughi, Chris Vargo, Onur Varol, Brian E. Weeks, John Wihbey, Thomas J. Wood, and Kai-Cheng Yang. 2020. Tackling misinformation: What researchers could do with social media data. Harv. Kenn. School Misinf. Rev. 1, 8 (2020).Google ScholarCross Ref
[47] Pattanaphanchai Jarutas, O’Hara Kieron, and Hall Wendy. 2013. Trustworthiness criteria for supporting users to assess the credibility of web information. In Proceedings of the 22nd International Conference on World Wide Web (WWW’13 Companion). Association for Computing Machinery, New York, NY, 1123–1130. DOI:Google ScholarDigital Library
[48] Peikari Cyrus and Chuvakin Anton. 2004. Security Warrior: Know Your Enemy. O’Reilly Media, Inc.Google Scholar
[49] Pennycook Gordon, Cannon Tyrone D., and Rand David G.. 2018. Prior exposure increases perceived accuracy of fake news. J. Experim. Psychol.: Gen. 147, 12 (2018), 1865.Google ScholarCross Ref
[50] Pennycook Gordon, Epstein Ziv, Mosleh Mohsen, Arechar Antonio A., Eckles Dean, and Rand David G.. 2021. Shifting attention to accuracy can reduce misinformation online. Nature 592, 7855 (01 Apr. 2021), 590–595. DOI:Google ScholarCross Ref
[51] Pennycook Gordon and Rand David G.. 2019. Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc. Nat’l Acad. Sci. 116, 7 (2019), 2521–2526.Google ScholarCross Ref
[52] Pennycook Gordon and Rand David G.. 2019. Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition 188 (2019), 39–50.Google ScholarCross Ref
[53] Pennycook Gordon and Rand David G.. 2020. Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. J. Personal. 88, 2 (2020), 185–200.Google ScholarCross Ref
[54] Pereira Andrea, Harris Elizabeth, and Bavel Jay J. Van. 2023. Identity concerns drive belief: The impact of partisan identity on the belief and dissemination of true and false news. Group Process. Intergr. Relat. 26, 1 (2023), 24–47.Google ScholarCross Ref
[55] Pérez-Rosas Verónica, Kleinberg Bennett, Lefevre Alexandra, and Mihalcea Rada. 2018. Automatic detection of fake news. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, 3391–3401. Retrieved from https://www.aclweb.org/anthology/C18-1287Google Scholar
[56] Potthast Martin, Kiesel Johannes, Reinartz Kevin, Bevendorff Janek, and Stein Benno. 2018. A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 231–240. DOI:Google ScholarCross Ref
[57] Potthast Martin, Köpsel Sebastian, Stein Benno, and Hagen Matthias. 2016. Clickbait detection. In Advances in Information Retrieval, Ferro Nicola, Crestani Fabio, Moens Marie-Francine, Mothe Josiane, Silvestri Fabrizio, Nunzio Giorgio Maria Di, Hauff Claudia, and Silvello Gianmaria (Eds.). Springer International Publishing, Cham, 810–817. Google Scholar
[58] Puschmann Cornelius, Karakurt Hevin, Amlinger Carolin, Gess Nicola, and Nachtwey Oliver. 2022. RPC-Lex: A dictionary to measure German right-wing populist conspiracy discourse online. Convergence 28, 4 (2022), 1144--1171. DOI:Google ScholarCross Ref
[59] Rabinowitz George and Macdonald Stuart Elaine. 1989. A directional theory of issue voting. Am. Polit. Sci. Rev. 83, 1 (1989), 93–121.Google ScholarCross Ref
[60] Salazar Kim. 2020. Contextual inquiry: Inspire design by observing and interviewing users in their context. Niels. Norm. Group 6 (2020).Google Scholar
[61] Scheufele Dietram A. and Krause Nicole M.. 2019. Science audiences, misinformation, and fake news. Proc. Nat’l Acad. Sci. 116, 16 (2019), 7662–7669. DOI:Google ScholarCross Ref
[62] Shahid Farhana, Kamath Srujana, Sidotam Annie, Jiang Vivian, Batino Alexa, and Vashistha Aditya. 2022. “It matches my worldview”: Examining perceptions and attitudes around fake videos. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’22). Association for Computing Machinery, New York, NY, Article 255, 15 pages. DOI:Google ScholarDigital Library
[63] Sharma Karishma, Qian Feng, Jiang He, Ruchansky Natali, Zhang Ming, and Liu Yan. 2019. Combating fake news: A survey on identification and mitigation techniques. ACM Trans. Intell. Syst. Technol. 10, 3, Article 21 (Apr. 2019), 42 pages. DOI:Google ScholarDigital Library
[64] Shu Kai, Bernard H. Russell, and Liu Huan. 2019. Studying fake news via network analysis: Detection and mitigation. In Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining, Agarwal Nitin, Dokoohaki Nima, and Tokdemir Serpil (Eds.). Springer International Publishing, Cham, 43–65. DOI:Google ScholarCross Ref
[65] Shu Kai, Sliva Amy, Wang Suhang, Tang Jiliang, and Liu Huan. 2017. Fake news detection on social media: A data mining perspective. SIGKDD Explor. Newslett. 19, 1 (Sept. 2017), 22–36. DOI:Google ScholarDigital Library
[66] Skitalinskaya Gabriella, Klaff Jonas, and Wachsmuth Henning. 2021. Learning from revisions: Quality assessment of claims in argumentation at scale. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Association for Computational Linguistics, 1718–1729. DOI:Google ScholarCross Ref
[67] Sloman S. and Fernbach P.. 2017. The Knowledge Illusion: Why We Never Think Alone. Penguin Publishing Group. Retrieved from https://books.google.dk/books?id=2xuMDAAAQBAJGoogle Scholar
[68] Spence Michael. 1978. Job market signaling. In Uncertainty in Economics. Elsevier, 281–306.Google ScholarCross Ref
[69] Starbird Kate, Arif Ahmer, and Wilson Tom. 2019. Disinformation as collaborative work: Surfacing the participatory nature of strategic information operations. Proc. ACM Hum.-comput. Interact. 3, CSCW, Article 127 (Nov. 2019), 26 pages. DOI:Google ScholarDigital Library
[70] Project The Trust. 2017. The 8 Trust Indicators. Retrieved from https://thetrustproject.org/trust-indicators/Google Scholar
[71] Thompson Stuart A. and Alba Davey. 2022. Fact and mythmaking blend in Ukraine’s Information War. Retrieved from https://www.nytimes.com/2022/03/03/technology/ukraine-war-misinfo.html?action=click&module=RelatedLinks&pgtype=ArticleGoogle Scholar
[72] Vosoughi Soroush, Roy Deb, and Aral Sinan. 2018. The spread of true and false news online. Science 359, 6380 (2018), 1146–1151.Google ScholarCross Ref
[73] Community W3C. 2020. Reviewed Credibility Signals. Retrieved from https://credweb.org/reviewed-signals-20200224Google Scholar
[74] Wachsmuth Henning, Naderi Nona, Hou Yufang, Bilu Yonatan, Prabhakaran Vinodkumar, Thijm Tim Alberdingk, Hirst Graeme, and Stein Benno. 2017. Computational argumentation quality assessment in natural language. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Association for Computational Linguistics, 176–187. Retrieved from https://aclanthology.org/E17-1017Google ScholarCross Ref
[75] Wang William Yang. 2017. “Liar, liar pants on fire”: A new benchmark dataset for fake news detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 422–426. DOI:Google ScholarCross Ref
[76] Wardle Claire. 2018. Fake news. it’s complicated. Retrieved from https://medium.com/1st-draft/fake-news-its-complicated-d0f773766c79Google Scholar
[77] Wardle Claire and Derakhshan Hossein. 2018. Thinking about “information disorder”: Formats of misinformation, disinformation, and mal-information. Ireton, Cherilyn; Posetti, Julie. Journalism, “Fake News” & Disinformation. Paris: UNESCO (2018), 43–54.Google Scholar
[78] contributors Wikipedia. 2021. List of political parties in Germany—Wikipedia, the Free Encyclopedia. Retrieved from https://en.wikipedia.org/w/index.php?title=List_of_political_parties_in_Germany&oldid=1030256103Google Scholar
[79] contributors Wikipedia. 2022. Five whys—Wikipedia, the Free Encyclopedia. Retrieved from https://en.wikipedia.org/w/index.php?title=Five_whys&oldid=1102936260Google Scholar
[80] contributors Wikipedia. 2022. Impressum—Wikipedia, the Free Encyclopedia. Retrieved from https://en.wikipedia.org/w/index.php?title=Impressum&oldid=1092341470Google Scholar
[81] contributors Wikipedia. 2022. List of newspapers in the United States—Wikipedia, the Free Encyclopedia. Retrieved from https://en.wikipedia.org/w/index.php?title=List_of_newspapers_in_the_United_States&oldid=1101330486Google Scholar
[82] Wineburg Sam, Breakstone Joel, McGrew Sarah, Smith Mark D., and Ortega Teresa. 2022. Lateral reading on the open internet: A district-wide field study in high school government classes. J. Educat. Psychol. 114, 5 (2022).Google ScholarCross Ref
[83] Wineburg Sam and McGrew Sarah. 2019. Lateral reading and the nature of expertise: Reading less and learning more when evaluating digital information. Teachers Coll. Rec. 121, 11 (2019), 1–40. DOI:Google ScholarCross Ref
[84] Wood Thomas and Porter Ethan. 2019. The elusive backfire effect: Mass attitudes’ steadfast factual adherence. Polit. Behav. 41, 1 (2019), 135–163.Google ScholarCross Ref
[85] Organization World Health. 2020. Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation. Retrieved from https://www.who.int/news/item/23-09-2020-managing-the-covid-19-infodemic-promoting-healthy-behaviours-and-mitigating-the-harm-from-misinformation-and-disinformationGoogle Scholar
[86] Zahavi Amotz. 1975. Mate selection—A selection for a handicap. J. Theoret. Biol. 53, 1 (1975), 205–214.Google ScholarCross Ref
[87] Zhang Amy X., Ranganathan Aditya, Metz Sarah Emlen, Appling Scott, Sehat Connie Moon, Gilmore Norman, Adams Nick B., Vincent Emmanuel, Lee Jennifer, Robbins Martin, Ed Bice, Sandro Hawke, David Karger, and Xiao Mina. 2018. A structured response to misinformation: Defining and annotating credibility indicators in news articles. In Proceedings of the the Web Conference. 603–612.Google ScholarDigital Library

Index Terms

Reliability Criteria for News Websites
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
  2. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

Fake News, Disinformation, Propaganda, and Media Bias
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

The rise of Internet and social media changed not only how we consume information, but it also democratized the process of content creation and dissemination, thus making it easily available to anybody. Despite the hugely positive impact, this situation ...
Read More
Science Disinformation: On the Problem of Fake News
Abstract
This article is devoted to an important socio-cultural phenomenon that undermines public confidence in science, that is, fake science news. The term fake news is analyzed and data on the dissemination of fake news on social networks is provided. ...
Read More
Fake News, Disinformation, Propaganda, Media Bias, and Flattening the Curve of the COVID-19 Infodemic
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

The rise of social media has democratized content creation and has made it easy for anybody to share and to spread information online. On the positive side, this has given rise to citizen journalism, thus enabling much faster dissemination of ...
Read More

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Published in
ACM Transactions on Computer-Human Interaction Volume 31, Issue 2
April 2024
576 pages
ISSN:1073-0516
EISSN:1557-7325
DOI:10.1145/3613620
Editors:
Kristina Höök
KTH Royal Institute of Technology, Sweden
,
Kasper Hornbæk
University of Copenhagen, Denmark
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
- Published: 29 January 2024
- Online AM: 5 December 2023
- Accepted: 20 September 2023
- Revised: 18 September 2023
- Received: 2 November 2022
Published in tochi Volume 31, Issue 2

Permissions
Request permissions about this article.
Request Permissions

Check for updates
Author Tags
Misinformation
disinformation
fake news
criteria
media litearcy
checklist
online news
fact-checking
credibility
information quality
Qualifiers
- research-article
Conference
Funding Sources
Other Metrics
View Article Metrics

Article Metrics
- 0
  Total Citations
  View Citations
- 302
  Total Downloads
- Downloads (Last 12 months)302
- Downloads (Last 6 weeks)67
Other Metrics
View Author Metrics
Cited By
This publication has not been cited yet

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

View Full Text

Reliability Criteria for News Websites

ACM Transactions on Computer-Human Interaction

Abstract

REFERENCES

Cited By

Index Terms

Recommendations

Fake News, Disinformation, Propaganda, and Media Bias

Science Disinformation: On the Problem of Fake News

Fake News, Disinformation, Propaganda, Media Bias, and Flattening the Curve of the COVID-19 Infodemic