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Disinformation and social bot operations in the run up to the 2017 French presidential election
Open accessAbstract
Recent accounts from researchers, journalists, as well as federal investigators, reached a unanimous conclusion: social media are systematically exploited to manipulate and alter public opinion. Some disinformation campaigns have been coordinated by means of bots, social media accounts controlled by computer scripts that try to disguise themselves as legitimate human users. In this study, we describe one such operation that occurred in the run up to the 2017 French presidential election. We collected a massive Twitter dataset of nearly 17 million posts, posted between 27 April and 7 May 2017 (Election Day). We then set to study the MacronLeaks disinformation campaign: By leveraging a mix of machine learning and cognitive behavioral modeling techniques, we separated humans from bots, and then studied the activities of the two groups independently, as well as their interplay. We provide a characterization of both the bots and the users who engaged with them, and oppose it to those users who didn’t. Prior interests of disinformation adopters pinpoint to the reasons of scarce success of this campaign: the users who engaged with MacronLeaks are mostly foreigners with pre-existing interest in alt-right topics and alternative news media, rather than French users with diverse political views. Concluding, anomalous account usage patterns suggest the possible existence of a black market for reusable political disinformation bots.
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“Nowadays, bots can produce credible content with human-like temporal patterns [10, 11, 12].”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [11]
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“To identify automated agents in the data set, we developed a deep neural network model (see Methods and SI), which classified 13.4% of users as bots, a value compatible to estimations during other voting events [10, 11, 12].”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [11]
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
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
Experimental evidence of massive-scale emotional contagion through social networks
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320040111 · Cited by 3,687
Earthquake shakes Twitter users
Sakaki, T., Okazaki, M., & Matsuo, Y. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772777 · Cited by 3,631
Exposure to ideologically diverse news and opinion on Facebook
Bakshy, E., Messing, S., & Adamic, L. A. (2015) · Science · doi:10.1126/science.aaa1160 · Cited by 3,245
The spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692
A 61-million-person experiment in social influence and political mobilization
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012) · Nature · doi:10.1038/nature11421 · Cited by 2,575
The spreading of misinformation online
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517441113 · Cited by 2,407
Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016) · Communications of the ACM · doi:10.1145/2818717 · Cited by 1,973
Self-citationSocial Media and the Decision to Participate in Political Protest: Observations From Tahrir Square
Tüfekçi, Z., & Wilson, C. (2012) · Journal of Communication · doi:10.1111/j.1460-2466.2012.01629.x · Cited by 1,660
Research works printed but not linked: 5
- Allport, G. W., & Postman, L. (1947). The psychology of rumor. Oxford Press
- Gilani, Z., Wang, L., Crowcroft, J., Almeida, M., & Farahbakhsh, R. (2016, April). Stweeler: A Framework for Twitter Bot Analysis. In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 37-38). International World Wide Web Conferences Steering Committee.
- Kollanyi, B. (2016). Where Do Bots Come From? An Analysis of Bot Codes Shared on GitHub. International Journal of Communication, 10(20).
- Kollanyi, B., Howard, P. N., & Woolley, S. C. (2016). Bots and Automation over Twitter during the First U.S. Presidential Debate. Data Memo 2016.1. Oxford, UK: Project on Computational Propaganda.
- Savage, S., Monroy-Hernandez, A., & Höllerer, T. (2016). Botivist: Calling Volunteers to Action using Online Bots. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 813-822). ACM.
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Tenove, C., Buffie, J., McKay, S., & Moscrop, D. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3235819 · Cited by 50
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Morstatter, F., Shao, Y., Galstyan, A., & Karunasekera, S. A. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3188733 · Cited by 35
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Corò, F., Cruciani, E., D’Angelo, G., & Ponziani, S. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/29 · Cited by 33
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Nasim, M., Nguyen, A., Lothian, N., Cope, R. C., & Mitchell, L. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191574 · Cited by 32
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Dutt, R., Deb, A., & Ferrara, E. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-3582-2_12 · Cited by 31
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Cavelty, M. D., & Wenger, A. (2022) · Routledge eBooks · doi:10.4324/9781003110224 · Cited by 35
Bibliometric analysis of fake news indexed in Web of Science and Scopus (2001-2020)
Patra, R. K., Pandey, N., & Sudarsan, D. (2022) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-11-2021-0177 · Cited by 36
Li, S., Zhao, C., Li, Q., Huang, J., Zhao, D., & Zhu, P. (2022) · World Wide Web · doi:10.1007/s11280-022-01114-2 · Cited by 31
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Kušen, E., & Strembeck, M. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102126 · Cited by 29
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Zelenkauskaitė, A. (2021) · University of Michigan Press eBooks · doi:10.3998/mpub.12237294 · Cited by 32
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Glenski, M., Weninger, T., & Volkova, S. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2029 · Cited by 31
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Daouadi, K. E., Rebaï, R. Z., & Amous, I. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-19810-7_30 · Cited by 28
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Hitkul, Prabhu, A., Guhathakurta, D., Jain, J., Subramanian, M., Reddy, M., Sehgal, S., Karandikar, T., Gulati, A., Arora, U., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.06914 · Cited by 24
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Gorrell, G., Roberts, I. G., Greenwood, M., Bakir, M. E., Iavarone, B., & Bontcheva, K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01129-1_17 · Cited by 22
BotSlayer: real-time detection of bot amplification on Twitter
Hui, P.-M., Yang, K.-C., Torres-Lugo, C., Monroe, Z., McCarty, M., Serrette, B., Pentchev, V., & Menczer, F. (2019) · The Journal of Open Source Software · doi:10.21105/joss.01706 · Cited by 22
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Persily, N., & Tucker, J. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108890960.014 · Cited by 24
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Wang, Y., Gao, S., Li, N., & Yu, S. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101371 · Cited by 22
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Patuelli, A., & Saracco, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-34024-y · Cited by 27
Chawla, Y., Radziwon, A., Scaringella, L., Lazarczyk, E., Greco, M., Silveira, P. D., de Aguiar, E. P., Shen, Q., Will, M., & Kowalska-Pyzalska, A. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102720 · Cited by 21
Cai, M., Luo, H., Meng, X., & Cui, Y. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.875574 · Cited by 23
Early Identification of Pathogenic Social Media Accounts
Alvari, H., Shaabani, E., & Shakarian, P. (2018) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2018.8587339 · Cited by 22
Alves, J. L. D., Weitzel, L., Quaresma, P., Cardoso, C. E., & Cunha, L. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33904-3_7 · Cited by 20
Bozuyla, M., & Özçift, A. (2021) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.3818 · Cited by 21
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Huang, H., Tian, H., Zheng, X., Zhang, X., Zeng, D. D., & Wang, F. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3396413 · Cited by 23
Multi-modal Social Bot Detection: Learning Homophilic and Heterophilic Connections Adaptively
Li, S., Qiao, B., Li, K., Lu, Q., Lin, M., & Zhou, W. J. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612569 · Cited by 19
Exposure to social bots amplifies perceptual biases and regulation propensity
Yan, H. Y., Yang, K.-C., Shanahan, J. E., & Menczer, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-46630-x · Cited by 24
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Duan, J., Li, W., Bai, Q., Nguyen, M., Wang, X., & Jiang, J. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3545822 · Cited by 18
Lemke, T., & Habegger, M. W. (2022) · Journal of Global Security Studies · doi:10.1093/jogss/ogac004 · Cited by 18
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Shevtsov, A., Tzagkarakis, C., Antonakaki, D., & Ioannidis, S. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19349 · Cited by 18
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Kušen, E., & Strembeck, M. (2018) · International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS) · doi:10.5220/0006699500130022 · Cited by 18
Congosto, M. (2018) · Culture & History Digital Journal · doi:10.3989/chdj.2018.015 · Cited by 18
Hong, H., & Oh, H. J. (2020) · Sustainability · doi:10.3390/su12166515 · Cited by 18
The spread of misinformation by social botsPreprint
Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., & Menczer, F. (2017) · arXiv (Cornell University) · Cited by 18
Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on TwitterPreprint
Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04406 · Cited by 18
Self-regulation 2:0? A critical reflection of the European fight against disinformation
Shattock, E. (2021) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-73 · Cited by 17
A Comparative Study of Bot Detection Techniques With an Application in Twitter Covid-19 Discourse
Antenore, M., Camacho, J. M., & Panizzi, E. (2022) · Social Science Computer Review · doi:10.1177/08944393211073733 · Cited by 21
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Kušen, E., & Strembeck, M. (2019) · Online Social Networks and Media · doi:10.1016/j.osnem.2019.04.001 · Cited by 18
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Alhazbi, S. (2020) · IEEE Access · doi:10.1109/access.2020.3033666 · Cited by 17
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Rovito, L., Bonin, L., Manzoni, L., & Lorenzo, A. D. (2022) · Applied Sciences · doi:10.3390/app12125915 · Cited by 18
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Bello, B. S., Heckel, R., & Minku, L. L. (2018) · International Conference on Social Networks Analysis, Management and Security · doi:10.1109/snams.2018.8554675 · Cited by 10
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Akhtar, M. M., Masood, R., Ikram, M., & Kanhere, S. S. (2024) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3634737.3644998 · Cited by 16
Hawkes Process for Understanding the Influence of Pathogenic Social Media Accounts
Alvari, H., & Shakarian, P. (2019) · International Conference on Data Intelligence and Security (ICDIS) · doi:10.1109/icdis.2019.00013 · Cited by 19
User Engagement with Digital Deception
Glenski, M., Volkova, S., & Kumar, S. (2020) · Lecture notes in social networks · doi:10.1007/978-3-030-42699-6_3 · Cited by 19
Chowdhury, F. A., Saha, D., Hasan, M. R., Saha, K., & Mueen, A. (2021) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3487351.3492715 · Cited by 19
Hybrid Intelligence Strategies for Identifying, Classifying and Analyzing Political Bots
Orosa, B. G., Gamallo, P., Martín-Rodilla, P., & Martínez-Castaño, R. (2021) · Social Sciences · doi:10.3390/socsci10100357 · Cited by 16
Improving information spread by spreading groups
Sela, A., Milo, O., Kagan, E., & Ben‐Gal, I. (2019) · Online Information Review · doi:10.1108/oir-08-2018-0245 · Cited by 15
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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
- 2017-07-31
- ISSN
- 1396-0458 (ISSN-L)
- 1396-0466
- OpenAlex
- W2724523750
- Cited by
- 524
- References
- 65
- Citations by year
- 2026: 32 · 2025: 39 · 2024: 56 · 2023: 43 · 2022: 64 · 2021: 73 · 2020: 92 · 2019: 64 · 2018: 52 · 2017: 4
- FWCI
- 148.49
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- First Monday (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- First Monday (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Air Force Office of Scientific Research
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Communication › Social Media and Politics
- Keywords
- disinformation, social bots, 2017 French presidential election, Twitter, bot detection, human-bot interaction, public opinion manipulation, alt-right
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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