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What types of COVID-19 conspiracies are populated by Twitter bots?
Open accessAbstract
With people moving out of physical public spaces due to containment measures to tackle the novel coronavirus (COVID-19) pandemic, online platforms become even more prominent tools to understand social discussion. Studying social media can be informative to assess how we are collectively coping with this unprecedented global crisis. However, social media platforms are also populated by bots, automated accounts that can amplify certain topics of discussion at the expense of others. In this paper, we study 43.3M English tweets about COVID-19 and provide early evidence of the use of bots to promote political conspiracies in the United States, in stark contrast with humans who focus on public health concerns.
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“Twitter bots utilizing COVID-19 content to spread political conspiracies [34]”
From Future directions for chatbot research: An interdisciplinary research agenda (2021) · cites this paper as [34]
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Cited by
Sorted by citations
The COVID-19 social media infodemic
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“…social bots can pose significant security risks by influencing public opinion and disseminating false information (Shao et al. 2017), spreading rumors and conspiracy theories (Ferrara 2020), creating fake reputations, and suppressing political competitors (Pierri et al. 2020; Benkler et al. 2017).”
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The COVID‐19 pandemic and the search for structure: Social media and conspiracy theories
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“Bots are automated accounts that post only certain types of information, often in collaboration with other bots, to boost the visibility of certain hashtags, news sources, or storylines (Ferrara, 2020).”
COVID-19 misinformation and the 2020 U.S. presidential election
Chen, E., Chang, H.-C. H., Rao, A., Lerman, K., Cowan, G., & Ferrara, E. (2021) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-57 · Cited by 98
“The COVID-19 pandemic presents a novel chance to assess where health misinformation becomes political (Ferrara, 2020).”
Self-citationSalvi, C., Iannello, P., Cancer, A., McClay, M., Rago, S., Dunsmoor, J. E., & Antonietti, A. (2021) · Frontiers in Communication · doi:10.3389/fcomm.2020.562588 · Cited by 100
“…demonstrated that accounts with a high “bot score” (indexing the likelihood of being a bot, or fake account) promulgate conspiratorial narratives charged with alt-right ideology and are specifically oriented toward hateful and polarizing political ideologies (Stella et al., 2018; Ferrara, 2020).”
BackgroundAlomari, E. A., Katib, I. A., Albeshri, A. A., & Mehmood, R. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18010282 · Cited by 90
Public risk perception and emotion on Twitter during the Covid-19 pandemic
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An augmented multilingual Twitter dataset for studying the COVID-19 infodemic
Lopez, C. E., & Gallemore, C. T. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00825-0 · Cited by 67
“Ferrara (2020) studies the role of bots in amplifying COVID-19 conspiracy theories.”
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COVID-19 Conspiracy Theories Discussion on Twitter
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“Analysis has also revealed that “high bot score accounts are used to promote political conspiracies and divisive hashtags alongside with COVID-19 content” (Ferrara 2020 p. 17), while accounts likely run by humans focus more on health and public welfare.”
How COVID-19 Information Spread in U.S.? The Role of Twitter as Early Indicator of Epidemics
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GANBOT: a GAN-based framework for social bot detection
Najari, S., Salehi, M., & Farahbakhsh, R. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00800-9 · Cited by 51
Bots Amplify and Redirect Hate Speech in Online Discourse About Racism During the COVID-19 Pandemic
Uyheng, J., Bellutta, D., & Carley, K. M. (2022) · Social Media + Society · doi:10.1177/20563051221104749 · Cited by 53
“There is ample scientific knowledge of bot activity in online talk about the COVID-19 pandemic, especially regarding their spread of COVID-19 conspiracy theories (Ferrara, 2020; Moffitt et al., 2021) and more general low-credibility information (Himelein-Wachowiak et al.”
BackgroundInfluentialAI alignment: Assessing the global impact of recommender systems
Bojić, L. (2024) · Futures · doi:10.1016/j.futures.2024.103383 · Cited by 70
User perspectives on digital literacy as a response to misinformation
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On modeling the correlates of conspiracy thinking
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“Ferrara [20], for instance, used bot detection techniques to study 43.3 million COVID-19 related English tweets and observed how bots used COVID-19 as a vector to promote ideological hashtags that were often associated with the alt-right in the USA.”
BackgroundInfluentialA global comparison of social media bot and human characteristics
Ng, L. H. X., & Carley, K. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-96372-1 · Cited by 42
Can WhatsApp benefit from debunked fact-checked stories to reduce misinformation?
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“Furthermore, during the COVID-19 pandemic, there has been an uptick in rumours and conspiracies spreading through online platforms(Ferrara, 2020).”
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“Ferrara [10] analyzed 43.3 million English tweets and found the preferences of bots and humans in tweet contents by analyzing n-grams and hashtags.”
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“, social bots [68], and an investigation to relate bot behavior to suspension is left for future research, considering that a correlation between account suspension and increased bot likeness was already observed in other social and political discussions [69, 70].”
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Dreisiebner, S., März, S., & Mandl, T. (2021) · Journal of Documentation · doi:10.1108/jd-12-2020-0217 · Cited by 47
“Also, artificially created information by bots has an impact (Ferrara, 2020).”
Background“This is Fake! Shared it by Mistake”:Assessing the Intent of Fake News Spreaders
Zhou, X., Shu, K., Phoha, V. V., Liu, H., & Zafarani, R. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512264 · Cited by 38
Hays, C., Schutzman, Z., Raghavan, M., Walk, E., & Zimmer, P. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583214 · Cited by 29
“Bots have reportedly influenced the 2016 US Presidential Election [5, 38], the Brexit vote in the UK [4, 38], the spread of misinformation about COVID-19 [27] and financial markets [13, 54].”
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Scientometric analysis of ICT-assisted intelligent control systems response to COVID-19 pandemic
Sood, S. K., Rawat, K. S., & Kumar, D. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08788-3 · Cited by 28
“The active high-cited paper, by Ferrara [18] in 2020, demonstrates how social media tools such as Twitter were used to prompt political conspiracies amid the C-19 catastrophe.”
BackgroundDigital Civic Participation and Misinformation during the 2020 Taiwanese Presidential Election
Chang, H.-C. H., Haider, S., & Ferrara, E. (2021) · Media and Communication · doi:10.17645/mac.v9i1.3405 · Cited by 25
“In line with recent literature (Ferrara, 2020; Yang et al., 2019), we used the top 10 and bottom 10 percentiles of users to set apart likely human users from automated accounts, with Botscores of 0.06 and below for humans and 0.67 and above for bots.”
MethodInfluentialSelf-citationGamified Inoculation Against Misinformation in India: A Randomized Control Trial
Harjani, T., Basol, M., Roozenbeek, J., & van der Linden, S. (2023) · Journal of Trial and Error · doi:10.36850/e12 · Cited by 34
Hameleers, M., & Yekta, N. (2023) · Communication Research · doi:10.1177/00936502231189685 · Cited by 27
“In this context, hyper-partisan and alternative media platforms may contest conventional knowledge of mainstream media and politicians (e.g., Heft et al., 2020), or even disseminate conspiracies emphasizing alternative interpretations (Ferrara, 2020).”
Laor, T., & Lissitsa, S. (2022) · Online Information Review · doi:10.1108/oir-06-2021-0299 · Cited by 24
Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges
Trajkova, M., Alhakamy, A., Cafaro, F., Vedak, S., Mallappa, R., & Kankara, S. R. (2020) · Informatics · doi:10.3390/informatics7030035 · Cited by 21
Measuring the diffusion of conspiracy theories in digital information ecologies
Heft, A., & Buehling, K. (2022) · Convergence The International Journal of Research into New Media Technologies · doi:10.1177/13548565221091809 · Cited by 22
“…other coordinated networks, again, are theorized to be employed for different purposes, ranging from the spread of CTs and political disinformation (Ferrara, 2020; Giglietto et al., 2020; Graham et al., 2020; Shao et al., 2017) or simulating mass support for a political matter (Elmas et al.,…”
BackgroundThe use of emotions in conspiracy and debunking videos to engage publics on YouTube
Kim, S. J., & Chen, K. (2022) · New Media & Society · doi:10.1177/14614448221105877 · Cited by 30
Green, M. A., Musi, E., Rowe, F., Charles, D., Darlington‐Pollock, F., Kypridemos, C., Morse, A. P., Rossini, P., Tulloch, J. S. P., Davies, A. D., et al. (2021) · Big Data & Society · doi:10.1177/20539517211013869 · Cited by 22
“Bots – defined here as co-ordinated internet agents that simulate human activity to share content – have also played an important role, with greater likelihood of sharing misinformation (Ferrara, 2020; Yang et al., 2020).”
InfluentialMorita, P. P., Hussain, I. Z., Kaur, J., Lotto, M., & Butt, Z. A. (2023) · Journal of Medical Internet Research · doi:10.2196/44356 · Cited by 20
Comparing the Impact of Social Media Regulations on News Consumption
Etta, G., Cinelli, M., Galeazzi, A., Valensise, C. M., Quattrociocchi, W., & Conti, M. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3171391 · Cited by 25
“In the context of COVID-19, there was an extensive application of bots to influence public opinion by fueling conspiracy theories and promoting divisive hashtags [48], [49].”
BackgroundA Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media
Butt, M. J., Malik, A. K., Qamar, N., Yar, S., Malik, A. J., & Rauf, U. (2023) · Sensors · doi:10.3390/s23125543 · Cited by 20
“The dataset was collected by using Twitter API dated from 22 January 2020 to 13 March 2020.”
MethodSouthwick, L., Guntuku, S. C., Klinger, E. V., Pelullo, A., McCalpin, H. J., & Merchant, R. M. (2021) · International Review of Psychiatry · doi:10.1080/09540261.2020.1854195 · Cited by 18
“Researchers observed how Twitter bot accounts used #COVID-19 to promote ideological hashtags typically associated with the U.S. ’ s alt-right values (Ferrara, 2020).”
BackgroundHaunschild, R., Bornmann, L., Potnis, D. D., & Tahamtan, I. (2021) · Quantitative Science Studies · doi:10.1162/qss_a_00168 · Cited by 18
Investigating coordinated account creation using burst detection and network analysis
Bellutta, D., & Carley, K. M. (2023) · Journal of Big Data · doi:10.1186/s40537-023-00695-7 · Cited by 15
Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter
Wang, Y., Ling, C., & Stringhini, G. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579542 · Cited by 20
“A wealth of research has been conducted in the past two years to better understand the dynamics of COVID-19 related misinformation and its effect on our society and on public health measures [18, 26, 33, 51, 75, 76].”
BackgroundStrategies for the Analysis of Large Social Media Corpora: Sampling and Keyword Extraction Methods
Ortiz, A. M., & García-Gámez, M. (2023) · Corpus Pragmatics · doi:10.1007/s41701-023-00143-0 · Cited by 13
“…pandemic with a myriad of methodologies and objectives: among others, Mackey et al. (2020) use Twitter data to research symptoms associated with the disease, Pulido et al. (2020) pay attention to the spread of false information, and Ferrara (2020) focuses on the role played by bots in such process.”
BackgroundHarakawa, R., & Iwahashi, M. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3063820 · Cited by 14
“Researchers have raised concerns about misinfor-mation, myths, and conspiracies related to COVID-19 [4]–[6], [14], [15].”
BackgroundCOVID-19 infodemic on Facebook and containment measures in Italy, United Kingdom and New Zealand
Etta, G., Galeazzi, A., Hutchings, J. R., Smith, C. S. J., Conti, M., Quattrociocchi, W., & Riva, G. V. D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0267022 · Cited by 15
“Further efforts aimed at quantifying the magnitude and the effects of infodemic on social media investigating a broader amount of platforms [5, 27, 28].”
BackgroundMendoza, M., Valenzuela, S., Núñez-Mussa, E., Padilla, F., Providel, E., Campos, S., Bassi, R., Riquelme, A., Aldana, V., & López, C. (2023) · Applied Sciences · doi:10.3390/app13095347 · Cited by 13
Evolution of the plandemic communication network among serial participants on Twitter
Xu, Y., Sun, Y., Hagen, L., Patel, M., & Falling, M. (2021) · New Media & Society · doi:10.1177/14614448211050928 · Cited by 12
“This tool has been widely applied to detect social bots tweeting about coronavirus misinformation (Ahmed et al., 2020; Ferrara, 2020; Gruzd and Mai, 2020; Jamison et al., 2020).”
BackgroundMethodPolitical polarization on Twitter during the COVID-19 pandemic: a case study in Brazil
Brum, P. P. V., Teixeira, M. C., Vimieiro, R., Araújo, E. F. D. M., Meira, W., & Pappa, G. L. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00949-x · Cited by 15
“The bot detection literature is vast (Ferrara 2020), and a bot is usually identified based on their overall posting behavior during a specified period of time.”
BackgroundLiars and Trolls and Bots Online: The Problem of Fake Persons
Harris, K. R. (2023) · Philosophy & Technology · doi:10.1007/s13347-023-00640-9 · Cited by 18
“Bots have often been implicated in the spread of misinformation online (Bastos & Mercea, 2018, 2019; Broniatowski et al., 2018; Ferrara, 2020), and are often driven by state actors (Alsmadi & O’Brien, 2020; Prier, 2017).”
BackgroundMechanisms Driving Online Vaccine Debate During the COVID-19 Pandemic
Seckin, O. C., Atalay, A., Otenen, E., Duygu, U., & Varol, O. (2024) · Social Media + Society · doi:10.1177/20563051241229657 · Cited by 19
“…anti-vaccine propaganda was mostly supported with false narratives and conspiracies, and the prevalence of anti-vaccine misinformation was conspicuous on social media platforms (Cha et al., 2021; Ferrara, 2020a; Gallotti et al., 2020; Gruzd et al., 2021; S. K. Lee et al., 2022; Singh et al., 2022).”
BackgroundLeveraging Natural Language Processing to Mine Issues on Twitter During the COVID-19 Pandemic
Agarwal, A., Salehundam, P., Padhee, S., Romine, W. L., & Banerjee, T. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378028 · Cited by 11
“Previous works have focused on analyzing online behavior and reactions to the spread of COVID-19 [6], or investigating conspiracy theories and social activism [8], [9].”
BackgroundA repeated-measures study on emotional responses after a year in the pandemic
Mozes, M., van der Vegt, I., & Kleinberg, B. (2021) · Scientific Reports · doi:10.1038/s41598-021-02414-9 · Cited by 14
“Examples include the spread of misinformation on Twitter [12,13], emotional consequences of lockdowns studied through Twitter (and Weibo) data [14], as well as the association between US partisanship and sentiment towards COVID-19 measures [15].”
BackgroundInfluentialSharma, S., Sharma, R. R., & Datta, A. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3225216 · Cited by 12
“Naturally, this amplified the discussions around Covid-19 vaccines both offline and online.”
BackgroundDispelling the Fake: Social Bot Detection Based on Edge Confidence Evaluation
Qiao, B., Zhou, W. J., Li, K., Li, S., & Hu, S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3396192 · Cited by 17
“They spread misinformation about COVID-19 [10], [11] and financial markets [12], [13].”
Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study
Rao, A., Sabri, N., Guo, S., Raschid, L., & Lerman, K. (2024) · Journal of Medical Internet Research · doi:10.2196/63910 · Cited by 13
A graph neural architecture search approach for identifying bots in social media
Tzoumanekas, G., Chatzianastasis, M., Ilias, L., Kiokes, G. C., Psarras, J. E., & Askounis, D. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1509179 · Cited by 10
“Throughout the COVID-19 pandemic [16], bots spread misinformation about the virus and the vaccines on social media, leading to mob panic, confusion, and even resistance to public health measures.”
Daube, D., & Ruhrmann, G. (2020) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-020-03249-x · Cited by 10
Jarvis, C. M., & Eddington, S. M. (2021) · New Media & Society · doi:10.1177/14614448211051648 · Cited by 10
Evolution of Malicious Social Bot Detection: From Individual Profiling to Group Analysis and Beyond
Liu, Y., Xu, S., Li, Y., & Yu, S. (2025) · Journal of Social Computing · doi:10.23919/jsc.2025.0017 · Cited by 9
DNA-influenced automated behavior detection on twitter through relative entropy
Gilmary, R., Akila, V., Vaiyapuri, G., & Balamurali, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-11854-w · Cited by 11
“3, 4, 5, 6, 7 and 8 for various metrics.”
BackgroundManiou, T. A., & Papa, V. (2023) · Communication & Society · doi:10.15581/003.36.1.35-46 · Cited by 9
Analyzing Societal Impact of COVID-19: A Study During the Early Days of the Pandemic
Shanthakumar, S. G., Seetharam, A., & Ramesh, A. (2020) · IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00132 · Cited by 8
An Unsupervised Misinformation Detection Framework to Analyze the Users using COVID-19 Twitter Data
Dhiman, A., & Toshniwal, D. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378250 · Cited by 8
“Similarly, in [18] and [19], the authors analyzed the spread of coronavirus related misinformation due to bots and other automated accounts.”
BackgroundGeorge, J. O., Elayan, S., Sykora, M. D., Solter, M., Feick, R., Hewitt, C., Liu, Y., & Shankardass, K. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20176707 · Cited by 15
Bailo, F., Johns, A., & Rizoiu, M.-A. (2023) · Information Communication & Society · doi:10.1080/1369118x.2023.2205479 · Cited by 23
“…et al., 2021; Weber et al., 2022; Yang et al., 2021; Zola et al., 2022) some studies found evidence of suspected coordinated, inauthentic behaviour (Ferrara, 2020; Graham et al., 2020, 2021; Graham & Keller, 2020; Keller et al., 2020), with far-right accounts (QAnon and MAGA accounts) being identi…”
BackgroundBots Versus Humans: Discursive Activism During the Pandemic in the Iranian Twittersphere
Kermani, H., Makou, A. B., Tafreshi, A., Ghodsi, A. M., & Ataee, H. (2023) · Social Media + Society · doi:10.1177/20563051231216927 · Cited by 8
“In contrast to other contexts, such as the study by Duan et al. (2022) and Ferrara (2020), Iranian users (bot or human) were less inclined to propagate conspir-acy theories during this politically contentious period, choosing instead to channel their efforts into their political goals.”
BackgroundResultGuimar�ães, N., Figueira, Á., & Torgo, L. (2021) · Mathematics · doi:10.3390/math9222988 · Cited by 6
Dynamic Topic Modeling of Covid-19 Vaccine-Related Tweets
Sharma, A., Susan, S., Bansal, A., et al. (2022) · doi:10.1145/3528114.3528127 · Cited by 6
“Twitter received masses of tweets from all across the world about Covid-19 and its related problems [2].”
BackgroundTwitter Bot Detection using Social Network Analysis
Bui, T., & Potika, K. (2022) · International Conference on Transdisciplinary AI (TransAI) · doi:10.1109/transai54797.2022.00022 · Cited by 6
“The malicious usage of bots was the source of misinformation on social media [1], spread of conspiracy theories [2], and political election interference [3].”
BackgroundInterdisciplinary Analysis of Science Communication on Social Media during the COVID-19 Crisis
Mandl, T., Jaki, S., Mitera, H., & Schmidt, F. (2023) · Knowledge · doi:10.3390/knowledge3010008 · Cited by 6
COVİD-19 AŞILARINA YÖNELİK ÜRETİLEN YENİ MEDYA İÇERİKLERİNİN POST-TRUTH KAVRAMI BAĞLAMINDA ANALİZİ
KARAKAŞ, O., & Doğru, Y. B. (2021) · ASYA STUDIES · doi:10.31455/asya.878400 · Cited by 6
A Framework for Topic Evolution and Tracking Their Sentiments With Time
Pradhan, R., & Sharma, D. K. (2022) · International Journal of Fuzzy System Applications · doi:10.4018/ijfsa.296589 · Cited by 5
Shahrezaye, M., Meckel, M., Steinacker, L., & Suter, V. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-73100-7_73 · Cited by 5
Misinformation and the demonization of human Rights: the Jordanian Child Rights Law
Hilal, G., Hilal, T., & Al-Fawareh, M. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2329417 · Cited by 5
“…fast in addressing several issues connected to misinformation, such as its impact on opinion formation (Shahi et al., 2021), conspiracy theories, and hate speech (Tahmasbi et al., 2021), the power of automated bots and accounts (Ferrara, 2020), and the impact on human rights (Abrusci et al., 2020).”
Fleerackers, A., Riedlinger, M. E., Bruns, A., & Burgess, J. (2022) · Media International Australia · doi:10.1177/1329878x221145022 · Cited by 5
“(Ferrara, 2020; see Rodríguez-Ruiz et al., 2020; Shevtsov et al., 2020).”
Digital Narratives of COVID-19: A Twitter Dataset for Text Analysis in Spanish
Torrent, S. A., Riande, G. D. R., Bonnell, J., et al. (2021) · Journal of Open Humanities Data · doi:10.5334/johd.28 · Cited by 4
Content Analysis in the Research Field of Disinformation
Staender, A., & Humprecht, E. (2022) · Springer eBooks · doi:10.1007/978-3-658-36179-2_29 · Cited by 4
Lost in Transmission: Investigating Filtering of COVID-19 Websites
Vyas, A., Raman, R. S., Ceccio, N., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-662-64331-0_22 · Cited by 4
“Several studies show that bots and ordinary users promote misinformation on social media [6,9,18,41].”
BackgroundTackling the Information Overload
Vogler, D., & Meißner, F. (2022) · doi:10.1002/9781119678953.ch4 · Cited by 4
Recognising and addressing health misinformation in nursing practice
Sharman, J. (2023) · Primary Health Care · doi:10.7748/phc.2023.e1791 · Cited by 3
The Role of Social Media as a Source of Information and Perception of Insecurity
Cuenca, M. D. C. S., & Pérez, N. E. (2025) · European Journal on Criminal Policy and Research · doi:10.1007/s10610-025-09628-x · Cited by 3
Misinformation detection on YouTube using video captions
Jagtap, R., Kumar, A., Goel, R., et al. (2026) · Online Social Networks and Media · doi:10.1016/j.osnem.2026.100345 · Cited by 3
“However, in [35]–[37], the authors argue that a substantial percentage of conspiratorial information remains online on YouTube and other platforms, influencing the public despite so much research.”
Conspiracy Theories Dissemination on SNS Vkontakte: COVID-19 Case
Platonov, K., & Svetlov, K. V. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-67238-6_23 · Cited by 3
Operation gridlock: opposite sides, opposite strategies
Babcock, M., & Carley, K. M. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-021-00133-9 · Cited by 3
My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection
Wang, L.-J., Qiao, X., Xie, Y.-W., Nie, W.-Z., Zhang, Y., & Liu, A.-A. (2023) · ACM Multimedia · doi:10.1145/3581783.3612396 · Cited by 13
Rahman, A., Mohammadi, E., & Alhoori, H. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100085 · Cited by 3
Beyond Words: Stylometric Analysis for Detecting AI Manipulation on Social Media
Ullah, U., Laudanna, S., Vinod, P., Sorbo, A. D., Visaggio, C. A., & Canfora, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70879-4_11 · Cited by 3
Understanding the Societal Disruption due to COVID-19 via User Tweets
Shanthakumar, S. G., Seetharam, A., & Ramesh, A. (2021) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp52413.2021.00039 · Cited by 3
Are Bots Humans? Analysis of Bot Accounts in 2019 Indian Lok Sabha Elections (Workshop Paper)
Hitkul, H., Gurjar, O., Sadaria, A., Gupta, K., Srikanth, S., Shah, R. R., & Kumaraguru, P. (2020) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm50055.2020.00073 · Cited by 3
“The 2016 United States Presidential Election [6], [13], [14], The 2018 United States Midterm Election [15], [16], [17], Germany’s Federal Election [18], [7], French Presidential Election [8] and large political events around the world [20], [21], [27].”
BackgroundMethodResultInfluentialAnalysis of COVID-19 Misinformation in Social Media using Transfer Learning
Dhankar, A., Samuel, H., Hassan, F., Farruque, N., Bolduc, F. V., & Zai͏̈ane, O. R. (2021) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai52525.2021.00141 · Cited by 3
“There are a number of topics fueling COVID-related misinformation, ranging from conspiracy theories, misreporting of morbidity and mortality, disease spread mechanisms, prevention meth-ods, treatments and drugs, recovery experiences, and political controversies [3], [4].”
BackgroundAn Analysis of Conspiracy Beliefs of Covid-19 Vaccination in Ghana
Baffoe, G. A., & Watson-Nortey, N. K. L. (2021) · Advances in Journalism and Communication · doi:10.4236/ajc.2021.94014 · Cited by 4
Metadata
- Authors
- Ferrara, E.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Ferrara, Emilio
- ORCID
- Ferrara, Emilio
- Venue
- First Monday
- Publisher
- University of Illinois Libraries
- Published
- 2020-05-19
- ISSN
- 1396-0458 (ISSN-L)
- 1396-0466
- OpenAlex
- W3032981809
- Cited by
- 223
- References
- 86
- Citations by year
- 2026: 7 · 2025: 18 · 2024: 20 · 2023: 43 · 2022: 43 · 2021: 56 · 2020: 34
- FWCI
- 58.73
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- First Monday (publishedVersion)
- First Monday (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- COVID-19 pandemic, Twitter bots, political conspiracy, misinformation, automated accounts, social media, public health discourse, online political discussion, United States
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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