Disinformation, cognitive biases and generative artificial intelligence: the ABCD Model (Analyse, Search, Contrast and Doubt) for critical information verification
DOI:
https://doi.org/10.3145/infonomy.26.033Keywords:
Disinformation, Infodemic, Media literacy, Cognitive biases, Critical thinking, Generative artificial intelligence, Deepfakes, Discourse analysis, Information Verification, Fact-checking, Framing, Digital ecosystemAbstract
Digital transformation has reshaped how information is produced, circulated and interpreted, placing disinformation among the main risks to democratic societies. This paper examines the narratives underlying disinformation processes in digital media from an interdisciplinary perspective that integrates communication studies, cognitive psychology, information science and artificial intelligence (AI) research. Building on a review of recent scientific literature and the analysis of five representative cases —Cambridge Analytica, the COVID-19 infodemic, the Southport riots, Pizzagate and the Zelensky deepfake—, the study examines how the effectiveness of information manipulation depends on the narrative architecture of the message, the cognitive biases of the receiver, and the algorithmic logic of digital platforms. Based on these findings, the paper proposes a discourse-analysis model for critical verification, the ABCD Model (Analyse, Búsqueda/Search, Contrast, and Doubt), which integrates the classic elements of the communicative process with the evaluation of cognitive biases and algorithmic architecture. The paper concludes that the shift from an information society towards a verification society requires a critical media literacy adapted to the era of generative AI.
References
Aral, S. (2020). The Hype Machine. Currency. ISBN: 978 0 525574583
Bakir, V.; McStay, A. (2018). Fake news and the economy of emotions. Digital Journalism, v. 6, n. 2, pp. 154-175. https://doi.org/10.1080/21670811.2017.1345645
Bakshy, E.; Messing, S.; Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, v. 348, n. 6239, pp. 1130-1132. https://doi.org/10.1126/science.aaa1160
Benkler, Y.; Faris, R.; Roberts, H. (2018). Network propaganda: Manipulation, disinformation, and radicalization in American politics. Oxford University Press. ISBN: 978 0 190923624
Bommasani, R.; Hudson, Drew A.; Adeli, Ehsan; Altman, Russ; Arora, Simran; Von Arx, Sydney; Bernstein, Michael S.; Bohg, Jeannette; Bosselut, Antoine; Brunskill, Emma et al. (2021). On the opportunities and risks of foundation models. Stanford CRFM. arXiv. https://doi.org/10.48550/arXiv.2108.07258
Castells, M. (2009). Communication power. Oxford University Press. ISBN: 978 0 199567041
Chesney, R.; Citron, D. K. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, v. 107, n. 6, pp. 1753-1819. https://doi.org/10.2139/ssrn.3213954
Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, v. 43, n. 4, pp. 51-58. https://doi.org/10.1111/j.1460-2466.1993.tb01304.x
European Commission (2018). A multi-dimensional approach to disinformation. Publications Office of the European Union. https://doi.org/10.2759/739290
European Union (2024). Artificial intelligence act. Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Fairclough, N. (1995). Critical discourse analysis: The critical study of language. Longman. ISBN: 978 0 582219847
Kahneman, D. (2012). Pensar rápido, pensar despacio. Debate. ISBN: 978 84 99921705
Kaplan, J.; McCandlish, Sam; Henighan, Tom; Brown, Tom B.; Chess, Benjamin; Child, Rewon; Gray, Scott; Radford, Alec; Wu, Jeffrey; Amodei, Dario (2020). Scaling laws for neural language models. arXiv. https://doi.org/10.48550/arXiv.2001.08361
Lazer, D. M. J.; Baum, M. A.; Benkler, Y.; Berinsky, A. J.; Greenhill, K. M.; Menczer, F.; Metzger, M.J.; Nyhan, B.; Pennycook, G.; Rothschild, D.; Schudson, M.; Sloman, S. A.; Sunstein, C. R.; Thorson, E. A.; Watts, D. J.; Zittrain, J. L. (2018). The science of fake news. Science, v. 359, n. 6380, pp. 1094-1096. https://doi.org/10.1126/science.aao2998
Lewandowsky, S.; Ecker, U. K. H.; Cook, J. (2017). Beyond misinformation: Understanding and coping with the post-truth era. Journal of Applied Research in Memory and Cognition, v. 6, n. 4, pp. 353-369. https://doi.org/10.1016/j.jarmac.2017.07.008
McCombs, M. E.; Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, v. 36, n. 2, pp. 176-187. https://doi.org/10.1086/267990
Mirsky, Y.; Lee, W. (2021). The creation and detection of deepfakes: A survey. ACM Computing Surveys, v. 54, n. 1. https://doi.org/10.1145/3425780
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, v. 2, n. 2, pp. 175-220. https://doi.org/10.1037/1089-2680.2.2.17
OECD (2024). Facts not fakes: Tackling disinformation, strengthening information integrity. OECD Publishing. https://doi.org/10.1787/d909ff7a-en
Pennycook, G.; Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, v. 188, pp. 39-50. https://doi.org/10.1016/j.cognition.2018.06.011
Sunstein, C. R. (2018). #Republic: Divided democracy in the age of social media. Princeton University Press. https://doi.org/10.2307/j.ctv8xnhtd
Tandoc, E. C.; Lim, Z. W.; Ling, R. (2018). Defining “fake news”. Digital Journalism, v. 6, n. 2, pp. 137-153. https://doi.org/10.1080/21670811.2017.1360143
Thorndike, E. L. (1920). A constant error in psychological ratings. Journal of Applied Psychology, v. 4, n. 1, pp. 25-29. https://doi.org/10.1037/h0071663
Tversky, A.; Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, v. 185, n. 4157, pp. 1124-1131. https://doi.org/10.1126/science.185.4157.1124
UNESCO (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Van Dijck, J.; Poell, T.; De Waal, M. (2018). The platform society: Public values in a connective world. Oxford University Press. https://doi.org/10.1093/oso/9780190889760.001.0001
Van Dijk, T. A. (1998). Ideology: A multidisciplinary approach. Sage Publications. ISBN: 978 0 761956542
Van Dijk, T. A. (2008). Discourse and power. Palgrave Macmillan. ISBN: 978 1 4 03996254
Vosoughi, S.; Roy, D.; Aral, S. (2018). The spread of true and false news online. Science, v. 359, n. 6380, pp. 1146-1151. https://doi.org/10.1126/science.aap9559
Wardle, C.; Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe. ISBN 978-9287183842.
Westerlund, M. (2019). The emergence of deepfake technology: A review. Technology Innovation Management Review, v. 9, n. 11, pp. 40-53. https://doi.org/10.22215/timreview/1282
World Economic Forum (2024). Global risks report 2024. https://www.weforum.org/publications/global-risks-report-2024
World Health Organization (2020). Managing the COVID-19 Infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation. https://www.who.int/news/item/23-09-2020-managing-the-covid-19-infodemic-promoting-healthy-behaviours-and-mitigating-the-harm-from-misinformation-and-disinformation
Zannettou, S.; Sirivianos, M.; Blackburn, J.; Kourtellis, N. (2019). The web of false information: Rumors, fake news, hoaxes, clickbait, and various other shenanigans. Journal of Data and Information Quality, v. 11, n. 3. https://doi.org/10.1145/3309699
Zhou, X.; Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys, v. 53, n. 5. https://doi.org/10.1145/3395046
Downloads
Published
How to Cite
Downloads
Dimensions
Societal impact
Issue
Section
License
Copyright (c) 2026 Nuria San-Servando-Hernández
This work is licensed under a Creative Commons Attribution 4.0 International License.
