A Study on Information Disorders on Social Networks during the Chilean Social Outbreak and COVID-19 Pandemic
Open accessAbstract
Information disorders on social media can have a significant impact on citizens’ participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by professional fact-checkers in Chile between October 2019 and October 2021, a period marked by political and health crises. The study found that false information spreads faster and reaches more users than true information on Twitter and Facebook. Instagram, on the other hand, seemed to be less affected by this phenomenon. False information was also more likely to be shared by users with lower reading comprehension skills. True information, on the other hand, tended to be less verbose and generate less interest among audiences. This research provides valuable insights into the characteristics of misinformation and how it spreads online. By recognizing the patterns of how false information diffuses and how users interact with it, we can identify the circumstances in which false and inaccurate messages are prone to becoming widespread. This knowledge can help us to develop strategies to counter the spread of misinformation and protect the integrity of democratic processes.
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Research has demonstrated that misinformation spreads more widely, rapidly, and deeply than fact-checks or truthful information on social media.
“Research has demonstrated that misinformation spreads more widely, rapidly, and deeply than fact-checks or truthful information on social media (Burel et al., 2021; Mendoza et al., 2023; Shao et al., 2018; Vosoughi et al., 2018).”
From "Just Asking Questions": Doing Our Own Research on Conspiratorial Ideation by Generative AI Chatbots (2026) · cites this paper as Mendoza et al., 2023
Cites
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 5× · see citing works
“This finding corroborates prior results [5], showing that false contents reach more people than other kinds of content.”
BackgroundResultInfluentialConfirmation Bias: A Ubiquitous Phenomenon in Many Guises
Nickerson, R. S. (1998) · Review of General Psychology · doi:10.1037/1089-2680.2.2.175 · Cited by 7,267 · see citing works
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767 · see citing works
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., et al. (2018) · Science · doi:10.1126/science.aao2998 · Cited by 4,160 · see citing works
The disinformation order: Disruptive communication and the decline of democratic institutions
Bennett, W. L., & Livingston, S. G. (2018) · European Journal of Communication · doi:10.1177/0267323118760317 · Cited by 1,704 · see citing works
The psychological drivers of misinformation belief and its resistance to correction
Ecker, U. K. H., Lewandowsky, S., Cook, J., Schmid, P., Fazio, L. K., Brashier, N., Kendeou, P., Vraga, E. K., & Amazeen, M. A. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-021-00006-y · Cited by 1,372 · cited 1× · see citing works
“For example, Ecker et al. [41] surveyed information that illustrates resistance from social network users to correcting information, showing that it is challenging to change their minds after users adopt a stance.”
BackgroundPennycook, G., & Rand, D. G. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.02.007 · Cited by 1,360 · see citing works
Health Information on the Internet
Berland, G. K., Elliott, M. N., Morales, L. S., Algazy, J. I., Kravitz, R. L., Broder, M. S., Kanouse, D. E., Muñoz, J. A., Puyol, J.-A., Lara, M., et al. (2001) · JAMA · doi:10.1001/jama.285.20.2612 · Cited by 1,212 · see citing works
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184 · see citing works
Social bots distort the 2016 U.S. Presidential election online discussion
Bessi, A., & Ferrara, E. (2016) · First Monday · doi:10.5210/fm.v21i11.7090 · Cited by 993 · see citing works
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
Rashkin, H., Choi, E., Jang, J. Y., Volkova, S., & Choi, Y. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1317 · Cited by 917 · see citing works
Zubiaga, A., Liakata, M., Procter, R., Wong Sak Hoi, G., & Tolmie, P. (2016) · PLOS ONE · doi:10.1371/journal.pone.0150989 · Cited by 722 · see citing works
Horne, B. D., & Adalı, S. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14976 · Cited by 715 · see citing works
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks
Ma, J., Gao, W. G., & Wong, K. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1184 · Cited by 657 · see citing works
Misinformation: susceptibility, spread, and interventions to immunize the public
van der Linden, S. (2022) · Nature Medicine · doi:10.1038/s41591-022-01713-6 · Cited by 570 · see citing works
Resilience to Online Disinformation: A Framework for Cross-National Comparative Research
Humprecht, E., Esser, F., & Aelst, P. V. (2020) · The International Journal of Press/Politics · doi:10.1177/1940161219900126 · Cited by 476 · see citing works
Friggeri, A., Adamic, L., Eckles, D., & Cheng, J. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14559 · Cited by 382 · see citing works
Predicting information credibility in time-sensitive social media
Castillo, C., Mendoza, M., & Poblete, B. (2013) · Internet Research · doi:10.1108/intr-05-2012-0095 · Cited by 339 · see citing works
Self-citationExpressing emotion in text-based communication
Hancock, J. T., Landrigan, C., & Silver, C. (2007) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240764 · Cited by 332 · see citing works
Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes
Kumar, S., West, R., & Leskovec, J. (2016) · Proceedings of the 25th International Conference on World Wide Web · doi:10.1145/2872427.2883085 · Cited by 310 · cited 2× · see citing works
“[36], who showed that the true contents tended to be shorter.”
BackgroundValenzuela, S., Halpern, D., Katz, J., & Miranda, J. P. (2019) · Digital Journalism · doi:10.1080/21670811.2019.1623701 · Cited by 256 · see citing works
Self-citationArgyris, Y. A., Wang, Z., Kim, Y., & Yin, Z. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106443 · Cited by 224 · see citing works
What types of COVID-19 conspiracies are populated by Twitter bots?
Ferrara, E. (2020) · First Monday · doi:10.5210/fm.v25i6.10633 · Cited by 223 · see citing works
Revisiting the Epistemology of Fact-Checking
Amazeen, M. A. (2015) · Critical Review · doi:10.1080/08913811.2014.993890 · Cited by 192 · see citing works
Complex Spreading Phenomena in Social Systems
Lehmann, S., & Ahn, Y. (2018) · Computational social sciences · doi:10.1007/978-3-319-77332-2 · Cited by 192 · see citing works
Fake news propagates differently from real news even at early stages of spreading
Zhao, Z., Zhao, J., Sano, Y., Levy, O., Hideki Takayasu, C., Takayasu, M., Li, D., Wu, J., & Havlin, S. (2020) · EPJ Data Science · doi:10.1140/epjds/s13688-020-00224-z · Cited by 175 · see citing works
Checking how fact-checkers check
Lim, C. (2018) · Research & Politics · doi:10.1177/2053168018786848 · Cited by 166 · see citing works
The Need for Smarter Definitions and Practical, Timely Empirical Research on Information Disorder
Wardle, C. (2018) · Digital Journalism · doi:10.1080/21670811.2018.1502047 · Cited by 154 · see citing works
Negative impact of social media panic during the COVID-19 outbreak in India
Kadam, A. B., & Atre, S. R. (2020) · Journal of Travel Medicine · doi:10.1093/jtm/taaa057 · Cited by 137 · see citing works
Corona and value change. The role of social media and emotional contagion
Steinert, S. (2020) · Ethics and Information Technology · doi:10.1007/s10676-020-09545-z · Cited by 117 · see citing works
Nekmat, E. (2020) · Social Media + Society · doi:10.1177/2056305119897322 · Cited by 103 · see citing works
Cognitive and affective responses to political disinformation in Facebook
Barfar, A. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.07.026 · Cited by 93 · see citing works
A Parsimonious Language Model of Social Media Credibility Across Disparate Events
Mitra, T., Wright, G. P., & Gilbert, É. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998351 · Cited by 80 · see citing works
Гуо, Л. (2020) · Digital Journalism · doi:10.1080/21670811.2020.1766986 · Cited by 78 · see citing works
COVID-19 information disorder: six types of harmful information during the pandemic in Europe
Hansson, S., Orru, K., Torpan, S., Bäck, A., Kažemekaitytė, A., Meyer, S. F., Ludvigsen, J., Savadori, L., Galvagni, A., & Pigrée, A. (2021) · Journal of Risk Research · doi:10.1080/13669877.2020.1871058 · Cited by 75 · see citing works
A Downward Spiral? A Panel Study of Misinformation and Media Trust in Chile
Valenzuela, S., Halpern, D. M., & Araneda, F. (2021) · The International Journal of Press/Politics · doi:10.1177/19401612211025238 · Cited by 59 · see citing works
Self-citationJournalism, „Fake News“ and Disinformation: A Handbook for Journalism Education and Training
Grmuša, T. (2020) · Mostariensia · Cited by 58 · see citing works
Rumors at the Speed of Light? Modeling the Rate of Rumor Transmission During Crisis
Zeng, L., Starbird, K., & Spiro, E. S. (2016) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.1109/hicss.2016.248 · Cited by 47 · see citing works
Frau‐Meigs, D. (2022) · Digital Journalism · doi:10.1080/21670811.2022.2081863 · Cited by 43 · see citing works
Understanding and neutralising covid-19 misinformation and disinformation
Wang, Y., Bye, J., Bales, K., Gurdasani, D., Mehta, A., Abba‐Aji, M., Stückler, D., & McKee, M. (2022) · BMJ · doi:10.1136/bmj-2022-070331 · Cited by 42 · see citing works
King, A. (2020) · EMBO Reports · doi:10.15252/embr.202050817 · Cited by 38 · see citing works
Bots in Social and Interaction Networks
Mendoza, M., Tesconi, M., & Cresci, S. (2020) · ACM Transactions on Information Systems · doi:10.1145/3419369 · Cited by 36 · see citing works
Self-citationFrau‐Meigs, D. (2019) · Medijske studije · doi:10.20901/ms.10.19.1 · Cited by 30 · see citing works
From social interactivity to buying: an instagram user behaviour based on the S-O-R paradigm
Ric, T., & Benazić, D. (2022) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2021.2025124 · Cited by 29 · see citing works
COVID-19 as a turning point in the fight against disinformation
Butcher, P. (2021) · Nature Electronics · doi:10.1038/s41928-020-00532-2 · Cited by 28 · see citing works
Mendoza, M., Parra, D., & Soto, Á. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102366 · Cited by 25 · see citing works
Self-citationSarmiento, H., & Poblete, B. (2021) · ACM Symposium on Applied Computing · doi:10.1145/3412841.3442044 · Cited by 6 · see citing works
Hinojosa, J. A., Moreno, E. M., Ferré, P., & Pozo, M. A. (2018) · ANALES RANM · doi:10.32440/ar.2018.135.02.supl01.art04 · Cited by 2 · see citing works
Sistemas predictivos de legilibilidad del mensaje escrito : fórmula de perspicuidad
Pazos, F. S. (1992) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 51 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Unsupervised Framing Analysis for Social Media Discourse in Polarizing Events
Sarmiento, H., Córdova, R., Ortiz, J., Bravo-Márquez, F., dos Santos, M. L. B., & Valenzuela, S. (2025) · ACM Transactions on the Web · doi:10.1145/3711912 · Cited by 4 · see citing works
Self-citationCOVID-19 vaccine information disorder in Chile: a quantitative analysis of fact-checked articles
Cruz, A., Schulz, V., Arriagada, E., Montero-Liberona, C., & Kalergis, A. M. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1399336 · Cited by 3 · see citing works
A Credibility Divide? Discerning Truth From Misinformation in Chile
Bachmann, I., Valenzuela, S., Mujica, C., Labarca, C., Grassau, D., & Halpern, D. M. (2024) · International Journal of Public Opinion Research · doi:10.1093/ijpor/edae017 · Cited by 3 · see citing works
Self-citationWeb Scraping Chilean News Media: A Dataset for Analyzing Social Unrest Coverage (2019–2023)
Molina, I., Morales, J., & Keith, B. (2025) · Data · doi:10.3390/data10110174 · Cited by 2 · see citing works
Molina, I., Keith, B., & Matus, M. (2025) · Data · doi:10.3390/data10020013 · Cited by 2 · see citing works
Cross-Lingual Cross-Domain Transfer Learning for Rumor Detection
Providel, E., Mendoza, M., & Solar, M. (2025) · Future Internet · doi:10.3390/fi17070287 · Cited by 2 · see citing works
Self-citation"Just Asking Questions": Doing Our Own Research on Conspiratorial Ideation by Generative AI Chatbots
FitzGerald, K. M., Riedlinger, M., Bruns, A., Harrington, S., Graham, T., & Angus, D. (2026) · Media and Communication · doi:10.17645/mac.11337 · Cited by 1 · see citing works
“…social media users that Donald Trump staged his own assassination attempt in July 2024 (Donald Trump assassination conspiracy theory); 9. and finally, the idea that Donald Trump or his close advisors including Elon Musk rigged the 2024 election in his favour (2024 US election conspiracy theory).”
Cabieses, B., Alexandra Obach, K., Madrid, P., Blukacz, A., Sadler, M., Carreño, A. B., Campaña, C., & Mezones-Holguin, E. (2026) · Vaccine · doi:10.1016/j.vaccine.2026.128433 · Cited by 1 · see citing works
El fenómeno de la desinformación: Revisión de experiencias internacionales y en Chile (Informe 1)
Ramírez, P. A., Bachmann, I., Brossi, L., Elórtegui-Gómez, C., Escobar, M. J., Ibarra, P., Lara, J., Padilla, F., & Peña, P. (2023) · doi:10.61670/informe-1_2023 · Cited by 1 · see citing works
Self-citationClaims of ownership, claims of dignity
Frei, R., Cordero, R., Lang, B., Rozas, J., & Rodríguez, J. P. (2024) · Journal of Language and Politics · doi:10.1075/jlp.24097.fre
(2024) · Building trust in public institutions · doi:10.1787/9f3fef5c-en
My Private–Public Sphere: Women’s Information Strategies in Times of News Mistrust
Pavez, I., Saldaña, M., Lira, C. L., & Gheza, K. (2025) · Journalism & Mass Communication Quarterly · doi:10.1177/10776990251364777
Correa, T., Valenzuela, S., Bachmann, I., Jorquera, N. I. T., Valdés, N. S., & Pavez, I. (2026) · Social Media + Society · doi:10.1177/20563051261470740
Self-citation
- Authors
- Mendoza, M., Valenzuela, S., Núñez-Mussa, E., Padilla, F., Providel, E., Campos, S., Bassi, R., Riquelme, A., Aldana, V., & López, C.
- Affiliations
- Pontificia Universidad Católica de Chile (CL)
- Michigan State University (US)
- Universidad Técnica Federico Santa María (CL)
- Universidad de Talca (CL)
- Universidad San Sebastián (CL)
- Corresponding author
- Mendoza, Marcelo
- ORCID
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2023-04-25
- Volume
- 13
- Issue
- 9
- Pages
- 5347
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4366986851
- Cited by
- 13
- References
- 51
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 3 · 2023: 1
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Funders
- Scientific Information Program (ANID-PLU200009, ANID-ICN17_002, ANID-FB210017, FONDECYT 1200211, ANID-NCS2022_046, FONDECYT 1231582)
- Millennium Institute for Foundational Research on Data (IMFD) (ANID-PLU200009, ANID-ICN17_002, ANID-FB210017, FONDECYT 1200211, ANID-NCS2022_046, FONDECYT 1231582)
- National Center of Artificial Intelligence (CENIA) (ANID-PLU200009, ANID-ICN17_002, ANID-FB210017, FONDECYT 1200211, ANID-NCS2022_046, FONDECYT 1231582)
- ANID (ANID-PLU200009, ANID-ICN17_002, ANID-FB210017, FONDECYT 1200211, ANID-NCS2022_046, FONDECYT 1231582)
- Millennium Nucleus on Digital Inequalities and Opportunities (NUDOS) (ANID-PLU200009, ANID-ICN17_002, ANID-FB210017, FONDECYT 1200211, ANID-NCS2022_046, FONDECYT 1231582)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- misinformation, information disorder, social media, COVID-19 pandemic, Twitter, Facebook, Instagram, fact-checking, reading comprehension, information sharing, democratic participation, misinformation countermeasures
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