The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
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.
Cited by
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Items 401–500 of 522
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Miao, H., Liu, Z., & Gao, J. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00097 · Cited by 1
Was bin ich – und wenn ja, wie viele?
Pfaffenberger, F., Adrian, C., & Heinrich, P. (2019) · Springer eBooks · doi:10.1007/978-3-658-24824-6_5 · Cited by 1
Models and Algorithms for Election Control through Influence Maximization.
Mehrizi, M. A., Corò, F., Cruciani, E., D’Angelo, G., & Ponziani, S. (2019) · Padua Research Archive (University of Padova) · Cited by 1
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Leite, M. A. G. L., Guelpeli, M. V. C., & Santos, C. Q. (2020) · doi:10.5753/brasnam.2020.11161 · Cited by 1
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Kumar, S., Bansal, R., & Mehta, R. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-9651-3_3 · Cited by 1
Song, J., & Fiore, S. M. (2020) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181320641360 · Cited by 2
Cordeiro, D. F., Rocha, A. S., Vieira, L. M., Cassiano, K. K., & da Silva, N. R. (2021) · Em Questão · doi:10.19132/1808-5245273.42-67 · Cited by 2
Exploring the Controlled Experiment by Social Bots
Min, Y., Zhou, Y., Jiang, T., & Wu, Y. (2021) · Big data management · doi:10.1007/978-981-16-2609-8_11 · Cited by 1
The Role of Bots in the Disinformation Process in Brazilian Politics between 2014 and 2018
Santana, C., Nunes, A., & da Silva, F. Q. B. (2021) · Libri · doi:10.1515/libri-2020-0071 · Cited by 1
Han, Y. (2021) · NC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro) · Cited by 1
Vitkova, L., Chechulin, A. A., & Kotenko, I. V. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-87178-9_42 · Cited by 1
Modeling Bot Activity in the Online Political Communication
Филиппов, И. Б., Yureskul, E., Стукал, Д., & Ахременко, А. С. (2021) · International Conference Management of Large-Scale System Development (MLSD) · doi:10.1109/mlsd52249.2021.9600132 · Cited by 1
Toloknev, K. A. (2021) · Вестник Пермского университета Политология · doi:10.17072/2218-1067-2021-4-16-29 · Cited by 1
Graph Algorithms and Applications 4
Liotta, G., Tamassia, R., & Tollis, I. G. (2006) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789812773296 · Cited by 1
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Badawy, A., Addawood, A., Lerman, K., & Ferrara, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.01997 · Cited by 1
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Singh, M. K., Jawed, J. A., Alam, M. A., Raghuvanshi, K. K., & Sachin, S. K. (2022) · Research Square · doi:10.21203/rs.3.rs-1964791/v1 · Cited by 2
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Rossi, S. (2024) · doi:10.22439/phd.19.2024 · Cited by 2
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Tagarelli, A., & Interdonato, R. (2018) · SpringerBriefs in computer science · doi:10.1007/978-3-030-00229-9_9
Searle, R. (2018) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-5225-5094-5.ch015
Bot and Gender Identification: Textual Analysis of Tweets.
Oliveira, R. R., de Andrade, C. M. V., Figuerêdo, J. S. L., Rocha-Junior, J. B., Calumby, R. T., Silva, I. M. D. C., & Neto, A. M. D. S. (2019) · CLEF (Working Notes)
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KIRANMAI, M. V. S. V., & Haritha, D. S. N. (2019) · i-manager s Journal on Information Technology · doi:10.26634/jit.8.2.16259
Bots como agentes de expressão: regime de visibilidades e o poder de criar redes
Regattieri, L. L. (2019) · Revista Contracampo · doi:10.22409/contracampo.v38i3.28504
Volodenkov, S. V. (2019) · Przegląd Strategiczny · doi:10.14746/ps.2019.1.23
Understanding Propagation of Malicious Information Online
Alvari, H. (2020)
Maharaj, S., Qian, T., Ohiba, Z., & Hayes, W. B. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3017374
COVID‐19 Implications for Research and Education on Engineered Structures and Services
Mendonça, D. J., Gomes, J. O., Kijewski-Correa, T. L., Esnard, A., & Ramírez, J. A. (2020) · Journal of Critical Infrastructure Policy · doi:10.18278/jcip.1.2.6
Antenore, M., Camacho-Rodriguez, J. M., & Panizzi, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01148
Information Literacy in Nova Scotia: Systematic Mapping of High School Learning Outcomes
Munroe-Lynds, C.-L. (2021) · Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · doi:10.29173/cais1217
Social physicsPreprint
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2021) · arXiv (Cornell University)
Analysis of Fact-Checking Platforms
Algül, A., & Kuruoğlu, G. S. (2021) · Advances in human services and public health (AHSPH) book series · doi:10.4018/978-1-7998-8421-7.ch013
Unipampa, dos Santos, L. C., Bonito, M., Unipampa, Foletto, R., & UFSM (2021) · doi:10.16921/ciespal.23.29
Behavioral changes associated with interacting with bots on Twitter
Babutsitdze, Z., & Vincileoni, D. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.371
Persily, N., & Tucker, J. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108890960.001
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Persily, N., & Tucker, J. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108890960.015
Staender, A., & Humprecht, E. (2021) · DOCA - Database of Variables for Content Analysis · doi:10.34778/4d
Searle, R. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-7113-1.ch008
Alvari, H., Shaabani, E., & Shakarian, P. (2021) · SpringerBriefs in computer science · doi:10.1007/978-3-030-61431-7_1
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Lim, J., Tamura, M., Nitta, N., Nakamura, K., & Babaguchi, N. (2022) · Studies in autonomic, data-driven and industrial computing · doi:10.1007/978-981-19-1524-6_5
Kollányi, B. (2022) · CUB Ph.D. Dissertations (Corvinus University of Budapest) · doi:10.14267/phd.2022050
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Chaphekar, G. (2022) · doi:10.31979/etd.jztw-7tus
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(2004) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789812794741
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Rüsch, S., Behlendorf, M., Becker, M. H., Kudlek, R., Mohamed, H. H. E., Schoenitz, F., Jehl, L., & Kapitza, R. (2022) · International Middleware Conference · doi:10.1145/3528535.3565243
Couto, J. M. M., Reis, J. C. S., & Benevenuto, F. (2023) · Research Square · doi:10.21203/rs.3.rs-2757556/v1
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Benkler, Y., Faris, R., & Roberts, H. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780190923624.002.0006
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Milea, T. D., & Siminiuc, R. (2025) · RoEduNet Conference: Networking in Education and Research (RoEduNet) · doi:10.1109/roedunet68395.2025.11208498
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El-Mawass, N. (2020) · theses.fr (ABES) · doi:10.70675/4da28cc2z492dz4ef1z8221z98c8beb02143
Chaffin, A. (2023) · theses.fr (ABES) · doi:10.70675/5e4cd6f0z8697z4a0cz921dzd8592affa0a9
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Topic Modeling and Word Embedding
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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
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