Ethical, political and epistemic implications of machine learning (mis)information classification: insights from an interdisciplinary collaboration between social and data scientists

References

• Torabi Asr, Fatemeh, and Maite Taboada. 2019. “Big Data and Quality Data for Fake News and Misinformation Detection.” Big Data & Society 6 (1): 205395171984331. https://doi.org/10.1177/2053951719843310.
   Web of Science ®Google Scholar
• Barker, Alex, and Hannah Murphy. 2020. ‘YouTube Reverts to Human Moderators in Fight Against Misinformation’. Financial Times, 20 September 2020. https://www.ft.com/content/e54737c5-8488-4e66-b087-d1ad426ac9fa.
   Google Scholar
• Bhatt, Umang, Javier Antorán, Yunfeng Zhang, Q. Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Melançon, et al. 2021. “Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty.” In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 401–413. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3461702.3462571
   Google Scholar
• Bieler, Patrick, Milena D. Bister, Janine Hauer, Martina Klausner, Jörg Niewöhner, Christine Schmid, and Peter. Sebastian von. 2021. “Distributing Reflexivity Through Co-Laborative Ethnography.” Journal of Contemporary Ethnography 50 (1): 77–98. https://doi.org/10.1177/0891241620968271.
   Web of Science ®Google Scholar
• Binns, Reuben, Michael Veale, Max Van Kleek, and Nigel Shadbolt. 2017. “Like Trainer, Like Bot? Inheritance of Bias in Algorithmic Content Moderation.” In Social Informatics (Lecture Notes in Computer Science), edited by Giovanni Luca Ciampaglia, Afra Mashhadi, and Taha Yasseri, 405–415. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-67256-4_32
   Google Scholar
• Birhane, Abeba, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, and Michelle Bao. 2022. “The Values Encoded in Machine Learning Research.” In In 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ‘22, 173–184. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3531146.3533083
   Google Scholar
• Borup, Mads, Nik Brown, Kornelia Konrad, and Harro Van Lente. 2006. “The Sociology of Expectations in Science and Technology.” Technology Analysis & Strategic Management 18 (3–4): 285–298. https://doi.org/10.1080/09537320600777002.
   Web of Science ®Google Scholar
• Brown, Shea, Jovana Davidovic, and Ali Hasan. 2021. “The Algorithm Audit: Scoring the Algorithms That Score Us.” Big Data & Society 8 (1): 205395172098386. https://doi.org/10.1177/2053951720983865.
   Web of Science ®Google Scholar
• Carlson, Matt. 2017. Journalistic Authority: Legitimating News in the Digital Era. Columbia University Press.
   Google Scholar
• CDEI. 2021. ‘The Role of AI in Addressing Misinformationon Social Media Platforms’. Centre for Data Ethics and Innovation. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1008700/Misinformation_forum_write_up__August_2021__-_web_accessible.pdf.
   Google Scholar
• Cinelli, Matteo, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. 2021. “The Echo Chamber Effect on Social Media.” Proceedings of the National Academy of Sciences 9), https://doi.org/10.1073/pnas.2023301118.
   Google Scholar
• Collins, H. M., and Robert Evans. 2002. “The Third Wave of Science Studies.” Social Studies of Science 32 (2): 235–296. https://doi.org/10.1177/0306312702032002003.
   Web of Science ®Google Scholar
• Cui, Limeng, and Dongwon Lee. 2020. ‘CoAID: COVID-19 Healthcare Misinformation Dataset’. ArXiv:2006.00885 [Cs], November. http://arxiv.org/abs/2006.00885.
   Google Scholar
• D’Ignazio, Catherine, and Lauren F. Klein. 2020. Data Feminism. MIT Press.
   Google Scholar
• Domínguez Hernández, Andrés, and Richard Owen. forthcoming. ‘“We Have Opened a Can of Worms”: Reconciling Critique and Design of Algorithmic Systems Through Co-Ethnography’. Journal of Responsible Innovation, Critique in, of, and for Responsible Innovation,, no. Critique in, of, and for Responsible Innovation.
   Google Scholar
• Domínguez Hernández, Andrés, and Vassilis Galanos. 2023. ‘A Toolkit of Dilemmas: Beyond Debiasing and Fairness Formulas for Responsible AI/ML’. In IEEE International Symposium on Technology and Society 2022 (ISTAS22). IEEE. https://doi.org/10.48550/arXiv.2303.01930
   Google Scholar
• Dou, Yingtong, Kai Shu, Congying Xia, Philip S. Yu, and Lichao Sun. 2021. “User Preference-Aware Fake News Detection.” In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2051–2055. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3404835.3462990
   Google Scholar
• Duarte, Natasha, Emma Llanso, and Anna Loup. 2018. “‘Mixed Messages? The Limits of Automated Social Media Content Analysis.” In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR, 106–106. https://proceedings.mlr.press/v81/duarte18a.html
   Google Scholar
• D’Ulizia, Arianna, Maria Chiara Caschera, Fernando Ferri, and Patrizia Grifoni. 2021. ‘Repository of Fake News Detection Datasets’. 4TU.ResearchData. doi:10.4121/14151755.v1.
   Google Scholar
• Edelson, Laura, Minh-Kha Nguyen, Ian Goldstein, Oana Goga, Damon McCoy, and Tobias Lauinger. 2021. “Understanding Engagement with U.S. (Mis)Information News Sources on Facebook.” In Proceedings of the 21st ACM Internet Measurement Conference, IMC ‘21, 444–463. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3487552.3487859
   Google Scholar
• EPSRC. 2018. ‘Framework for Responsible Innovation’. 2018. https://www.ukri.org/about-us/epsrc/our-policies-and-standards/framework-for-responsible-innovation/.
   Google Scholar
• Fisher, Erik, Roop L. Mahajan, and Carl Mitcham. 2006. “Midstream Modulation of Technology: Governance from Within.” Bulletin of Science, Technology & Society 26 (6): 485–496. https://doi.org/10.1177/0270467606295402.
   Google Scholar
• Foley, Rider W., Olivier Sylvain, and Sheila Foster. 2022. “Innovation and Equality: An Approach to Constructing a Community Governed Network Commons.” Journal of Responsible Innovation 9 (1): 49–73. https://doi.org/10.1080/23299460.2022.2043681.
   Web of Science ®Google Scholar
• Forsythe, Diana E. 1993. “Engineering Knowledge: The Construction of Knowledge in Artificial Intelligence.” Social Studies of Science 23 (3): 445–477. https://doi.org/10.1177/0306312793023003002.
   Web of Science ®Google Scholar
• Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021. “Datasheets for Datasets.” Communications of the ACM 64 (12): 86–92. https://doi.org/10.1145/3458723.
   Google Scholar
• Geiger, R. Stuart, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, and Jenny Huang. 2020. “Garbage in, Garbage out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes from?” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* ‘20, 325–336. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3351095.3372862
   Google Scholar
• Gilbert, Thomas Krendl, and Yonatan Mintz. 2019. “Epistemic Therapy for Bias in Automated Decision-Making.” In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 61–67. AIES ‘19. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3306618.3314294
   Google Scholar
• Gillespie, Tarleton. 2020. “Content Moderation, AI, and the Question of Scale.” Big Data & Society 7 (2): 205395172094323. https://doi.org/10.1177/2053951720943234.
   Web of Science ®Google Scholar
• Google. 2023. ‘Google News Policies - Publisher Center Help’. 2023. https://support.google.com/news/publisher-center/answer/6204050.
   Google Scholar
• Gorwa, Robert, Reuben Binns, and Christian Katzenbach. 2020. “Algorithmic Content Moderation: Technical and Political Challenges in the Automation of Platform Governance.” Big Data & Society 7 (1): 205395171989794. https://doi.org/10.1177/2053951719897945.
   Web of Science ®Google Scholar
• Gradoń, Kacper T., Janusz A. Hołyst, Wesley R. Moy, Julian Sienkiewicz, and Krzysztof Suchecki. 2021. “Countering Misinformation: A Multidisciplinary Approach.” Big Data & Society 8 (1): 205395172110138. https://doi.org/10.1177/20539517211013848.
   Web of Science ®Google Scholar
• Gravanis, Georgios, Athena Vakali, Konstantinos Diamantaras, and Panagiotis Karadais. 2019. “Behind the Cues: A Benchmarking Study for Fake News Detection.” Expert Systems with Applications 128 (August): 201–213. https://doi.org/10.1016/j.eswa.2019.03.036.
   Google Scholar
• Graves, Lucas. 2017. “Anatomy of a Fact Check: Objective Practice and the Contested Epistemology of Fact Checking.” Communication, Culture & Critique 10 (3): 518–537. https://doi.org/10.1111/cccr.12163.
   Web of Science ®Google Scholar
• Groh, Matthew, Ziv Epstein, Chaz Firestone, and Rosalind Picard. 2022. “Deepfake Detection by Human Crowds, Machines, and Machine-Informed Crowds.” Proceedings of the National Academy of Sciences 119 (1): e2110013119. https://doi.org/10.1073/pnas.2110013119.
   PubMed Web of Science ®Google Scholar
• Hall, Kita. 1999. “Performativity.” Journal of Linguistic Anthropology 9 (1/2): 184–187. https://doi.org/10.1525/jlin.1999.9.1-2.184.
   Google Scholar
• Han, Yi, Shanika Karunasekera, and Christopher Leckie. 2020. ‘Graph Neural Networks with Continual Learning for Fake News Detection from Social Media’. ArXiv:2007.03316 [Cs], August. http://arxiv.org/abs/2007.03316.
   Google Scholar
• Haraway, Donna. 2013. “Simians, Cyborgs, and Women.” In Simians, Cyborgs, and Women, https://doi.org/10.4324/9780203873106.
   Google Scholar
• Harding, Sandra. 1995. “?Strong Objectivity?: A Response to the New Objectivity Question.” Synthese 104 (3): 331–349. https://doi.org/10.1007/BF01064504.
   Web of Science ®Google Scholar
• Hassan, Naeemul, Fatma Arslan, Chengkai Li, and Mark Tremayne. 2017. “Toward Automated Fact-Checking: Detecting Check-Worthy Factual Claims by ClaimBuster.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD ‘17, 1803–1812. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3097983.3098131
   Google Scholar
• Hoven, Jeroen van den. 2013. “Value Sensitive Design and Responsible Innovation.” In Responsible Innovation, 75–83. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118551424.ch4
   Google Scholar
• Hüllermeier, Eyke, and Willem Waegeman. 2021. “Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods.” Machine Learning 110 (3): 457–506. https://doi.org/10.1007/s10994-021-05946-3.
   Web of Science ®Google Scholar
• Jasanoff, Sheila. 2004. States of Knowledge: The Co-Production of Science and the Social Order. Routledge. https://doi.org/10.4324/9780203413845
   Google Scholar
• Jaton, Florian. 2021. “Assessing Biases, Relaxing Moralism: On Ground-Truthing Practices in Machine Learning Design and Application.” Big Data & Society 8 (1): 205395172110135. https://doi.org/10.1177/20539517211013569.
   Web of Science ®Google Scholar
• Jin, Zhiwei, Juan Cao, Han Guo, Yongdong Zhang, and Jiebo Luo. 2017. “Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs.” In Proceedings of the 25th ACM International Conference on Multimedia, MM ’17, 795–816. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3123266.3123454
   Google Scholar
• Lassiter, Luke E. 2005. The Chicago Guide to Collaborative Ethnography. University of Chicago Press.
   Google Scholar
• Latour, Bruno, and Steve Woolgar. 1986. Laboratory Life: The Social Construction of Scientific Facts. Sage Library of Social Research (Vol. 80). Beverly Hills: Sage Publications.
   Google Scholar
• Lee, Nayeon, Yejin Bang, Andrea Madotto, and Pascale Fung. 2020. ‘Misinformation Has High Perplexity’. ArXiv:2006.04666 [Cs], June. http://arxiv.org/abs/2006.04666.
   Google Scholar
• Lewis, Seth C., and Oscar Westlund. 2015. “Big Data and Journalism.” Digital Journalism 3 (3): 447–466. https://doi.org/10.1080/21670811.2014.976418.
   Web of Science ®Google Scholar
• Li, Yichuan, Bohan Jiang, Kai Shu, and Huan Liu. 2020. “Toward A Multilingual and Multimodal Data Repository for COVID-19 Disinformation.” 2020 IEEE International Conference on Big Data (Big Data), https://doi.org/10.1109/BigData50022.2020.9378472.
   Google Scholar
• Li, Yichuan, Bohan Jiang, Kai Shu, and Huan Liu. 2020b. ‘MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation’. ArXiv:2011.04088 [Cs], November. http://arxiv.org/abs/2011.04088.
   Google Scholar
• Lim, Chloe. 2018. “Checking How Fact-Checkers Check.” Research & Politics 5 (3): 205316801878684. https://doi.org/10.1177/2053168018786848.
   Web of Science ®Google Scholar
• Lynch, Michael. 2017. “STS, Symmetry and Post-Truth.” Social Studies of Science 47 (4): 593–599. https://doi.org/10.1177/0306312717720308.
   PubMed Web of Science ®Google Scholar
• Madraki, Golshan, Isabella Grasso, Jacqueline M. Otala, Yu Liu, and Jeanna Matthews. 2021. “Characterizing and Comparing COVID-19 Misinformation Across Languages, Countries and Platforms.” In Companion Proceedings of the Web Conference 2021, 213–23. WWW ‘21. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3442442.3452304
   Google Scholar
• Marres, Noortje. 2018. “Why We Can’t Have Our Facts Back.” Engaging Science, Technology, and Society 4 (July): 423–443. https://doi.org/10.17351/ests2018.188.
   Google Scholar
• Meta. 2020. ‘Here’s How We’re Using AI to Help Detect Misinformation’. 19 November 2020. https://ai.facebook.com/blog/heres-how-were-using-ai-to-help-detect-misinformation/.
   Google Scholar
• Miceli, Milagros, Tianling Yang, Laurens Naudts, Martin Schuessler, Diana Serbanescu, and Alex Hanna. 2021. “Documenting Computer Vision Datasets: An Invitation to Reflexive Data Practices.” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency FAccT ‘21, 161–172. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3442188.3445880
   Google Scholar
• Moats, David, and Nick Seaver. 2019. ““You Social Scientists Love Mind Games”: Experimenting in the “Divide” Between Data Science and Critical Algorithm Studies.” Big Data & Society 6 (1): 205395171983340. https://doi.org/10.1177/2053951719833404.
   Web of Science ®Google Scholar
• Monti, Federico, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M. Bronstein. 2019. ‘Fake News Detection on Social Media Using Geometric Deep Learning’. ArXiv:1902.06673 [Cs, Stat], February. http://arxiv.org/abs/1902.06673.
   Google Scholar
• Nielsen, Dan S., and Ryan McConville. 2022. “MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset.” In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR ‘22, 3141–3153. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3477495.3531744
   Google Scholar
• Nieminen, Sakari, and Valtteri Sankari. 2021. “Checking PolitiFact’s Fact-Checks.” Journalism Studies 22 (3): 358–378. https://doi.org/10.1080/1461670X.2021.1873818.
   Web of Science ®Google Scholar
• Nyhan, Brendan, and Jason Reifler. 2010. “When Corrections Fail: The Persistence of Political Misperceptions.” Political Behavior 32 (2): 303–330. https://doi.org/10.1007/s11109-010-9112-2.
   Web of Science ®Google Scholar
• Owen, Richard, Jack Stilgoe, Phil Macnaghten, Mike Gorman, Erik Fisher, and Dave Guston. 2013. “A Framework for Responsible Innovation.” In Responsible Innovation, 27–50. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118551424.ch2
   Google Scholar
• Owen, Richard, Mario Pansera, Phil Macnaghten, and Sally Randles. 2021. “Organisational Institutionalisation of Responsible Innovation.” Research Policy 50 (1): 104132. https://doi.org/10.1016/j.respol.2020.104132.
   Web of Science ®Google Scholar
• Park, Sungkyu, Jaimie Yejean Park, Jeong-han Kang, and Meeyoung Cha. 2021. “The Presence of Unexpected Biases in Online Fact-Checking.” Harvard Kennedy School Misinformation Review (January), https://doi.org/10.37016/mr-2020-53.
   Google Scholar
• Pérez-Rosas, Verónica, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2018. “Automatic Detection of Fake News.” In In Proceedings of the 27th International Conference on Computational Linguistics, 3391–3401. Santa Fe, New Mexico, USA: Association for Computational Linguistics. https://aclanthology.org/C18-1287.
   Google Scholar
• PolitiFact. 2021. ‘Archived Fact-Check: Tucker Carlson Guest Airs Debunked Conspiracy Theory That COVID-19 Was Created in a Lab’, 2021. https://www.politifact.com/li-meng-yan-fact-check/.
   Google Scholar
• Pollock, Neil, and Robin Williams. 2010. “E-Infrastructures: How Do We Know and Understand Them? Strategic Ethnography and the Biography of Artefacts.” Computer Supported Cooperative Work (CSCW) 19 (6): 521–556. https://doi.org/10.1007/s10606-010-9129-4.
   Web of Science ®Google Scholar
• Prasad, Amit. 2022. “Anti-Science Misinformation and Conspiracies: COVID–19, Post-Truth, and Science & Technology Studies (STS).” Science, Technology and Society 88 (April)), https://doi.org/10.1177/09717218211003413.
   Google Scholar
• Rashkin, Hannah, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. “Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking.” In In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2931–2937. Copenhagen, Denmark: Association for Computational Linguistics. https://doi.org/10.18653/v1/D17-1317
   Google Scholar
• Reporter’s Lab. 2022. ‘Database of Global Fact-Checking Sites’. Duke Reporters’ Lab. 2022. https://reporterslab.org/fact-checking/.
   Google Scholar
• Rietdijk, Natascha, and Alfred Archer. 2021. “Post-Truth, False Balance and Virtuous Gatekeeping.” In Virtues, Democracy, and Online Media: Ethical and Epistemic Issues, edited by Nancy Snow, and Maria Silvia Vaccarezza. Routledge.
   Google Scholar
• Sacco, Pier Luigi, Riccardo Gallotti, Federico Pilati, Nicola Castaldo, and Manlio De Domenico. 2021. “Emergence of Knowledge Communities and Information Centralization During the COVID-19 Pandemic.” Social Science & Medicine 285 (September): 114215. https://doi.org/10.1016/j.socscimed.2021.114215.
   PubMedGoogle Scholar
• Sayers, Freddie. 2021. ‘Facebook Censors Award-Winning Journalist for Criticising the WHO’. UnHerd, 11 February 2021. https://unherd.com/thepost/facebook-censors-award-winning-journalist-for-criticising-the-who/.
   Google Scholar
• Schuurbiers, Daan. 2011. “What Happens in the Lab: Applying Midstream Modulation to Enhance Critical Reflection in the Laboratory.” Science and Engineering Ethics 17 (4): 769–788. https://doi.org/10.1007/s11948-011-9317-8.
   PubMed Web of Science ®Google Scholar
• Seifert, Colleen M. 2017. “The Distributed Influence of Misinformation.” Journal of Applied Research in Memory and Cognition 6 (4): 397. https://doi.org/10.1016/j.jarmac.2017.09.003.
   Web of Science ®Google Scholar
• Selbst, Andrew D., Danah boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. “Fairness and Abstraction in Sociotechnical Systems.” In In Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. FAT* ‘19. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3287560.3287598
   Google Scholar
• Shu, Kai, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2020. “FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media.” Big Data 8 (3): 171–188. https://doi.org/10.1089/big.2020.0062.
   PubMed Web of Science ®Google Scholar
• Sloane, Mona, and Emanuel Moss. 2019. “AI’s Social Sciences Deficit.” Nature Machine Intelligence 1 (8): 330–331. https://doi.org/10.1038/s42256-019-0084-6.
   Google Scholar
• Stewart, Elizabeth. 2021. “Detecting Fake News: Two Problems for Content Moderation.” Philosophy & Technology 34 (4): 923–940. https://doi.org/10.1007/s13347-021-00442-x.
   PubMedGoogle Scholar
• Stilgoe, Jack, Richard Owen, and Phil Macnaghten. 2013. “Developing a Framework for Responsible Innovation.” Research Policy 42 (9): 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008.
   Web of Science ®Google Scholar
• Suchman, Lucy. 2002. “Located Accountabilities in Technology Production.” Scandinavian Journal of Information Systems 14 (2), https://aisel.aisnet.org/sjis/vol14/iss2/7.
   Google Scholar
• Tanweer, Anissa, Emily Kalah Gade, P. M. Krafft, and Sarah K. Dreier. 2021. “Why the Data Revolution Needs Qualitative Methods.” Harvard Data Science Review 3 (3), https://doi.org/10.1162/99608f92.eee0b0da.
   Google Scholar
• Thacker, Paul D. 2021. “The Covid-19 Lab Leak Hypothesis: Did the Media Fall Victim to a Misinformation Campaign?” BMJ 374 (July): n1656. https://doi.org/10.1136/bmj.n1656.
   PubMedGoogle Scholar
• Uscinski, Joseph E. 2015. “The Epistemology of Fact Checking (Is Still Naìve): Rejoinder to Amazeen.” Critical Review 27 (2): 243–252. https://doi.org/10.1080/08913811.2015.1055892.
   Web of Science ®Google Scholar
• Uscinski, Joseph E., and Ryden W. Butler. 2013. “The Epistemology of Fact Checking.” Critical Review 25 (2): 162–180. https://doi.org/10.1080/08913811.2013.843872.
   Web of Science ®Google Scholar
• Valverde-Albacete, Francisco J., and Carmen Peláez-Moreno. 2014. “100% Classification Accuracy Considered Harmful: The Normalized Information Transfer Factor Explains the Accuracy Paradox.” PLOS ONE 9 (1): e84217. https://doi.org/10.1371/journal.pone.0084217.
   PubMed Web of Science ®Google Scholar
• Vosoughi, Soroush, Deb Roy, and Sinan Aral. 2018. “The Spread of True and False News Online.” Science 359 (6380): 1146–1151. https://doi.org/10.1126/science.aap9559.
   PubMed Web of Science ®Google Scholar
• Walter, Nathan, Jonathan Cohen, R. Lance Holbert, and Yasmin Morag. 2020. “Fact-Checking: A Meta-Analysis of What Works and for Whom.” Political Communication 37 (3): 350–375. https://doi.org/10.1080/10584609.2019.1668894.
   Web of Science ®Google Scholar
• Yearley, Steven. 1999. “Computer Models and the Public's Understanding of Science.” Social Studies of Science 29 (6): 845–866. https://doi.org/10.1177/030631299029006002.
   Web of Science ®Google Scholar
• Zhou, Xinyi, Jindi Wu, and Reza Zafarani. 2020. “Lecture Notes in Computer Science.” Advances in Knowledge Discovery and Data Mining 12085 (April): 354–367. https://doi.org/10.1007/978-3-030-47436-2_27.
   Google Scholar
• Zuboff, Shoshana. 2019. The Age of Surveillance Capitalism: The Fight for the Future at the New Frontier of Power. London: Profile Books.
   Google Scholar