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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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“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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“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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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
Signorini, A., Segre, A. M., & Polgreen, P. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019467 · Cited by 1,234
Identifying influential and susceptible members of social networks
Aral, S., & Walker, D. (2012) · Science · doi:10.1126/science.1215842 · Cited by 1,047
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
Self-citationOnline Human-Bot Interactions: Detection, Estimation, and Characterization
Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 981 · cited 16×
“Second, to train our models we used a mixture of various types of bots provided by multiple studies (Varol, et al., 2017; Cresci, et al., 2017).”
BackgroundMethodInfluentialSelf-citationYou Are What You Tweet: Analyzing Twitter for Public Health
Paul, M., & Dredze, M. H. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14137 · Cited by 946
Harnessing Social Media for Health Promotion and Behavior Change
Korda, H., & Itani, Z. (2011) · Health Promotion Practice · doi:10.1177/1524839911405850 · Cited by 939
Media Manipulation and Disinformation Online
Lewis, B., & Marwick, A. E. (2017) · Cited by 932
BotOrNot: A System to Evaluate Social Bots
Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016) · Proceedings of the 25th International Conference Companion on World Wide Web - WWW 16 Companion · doi:10.1145/2872518.2889302 · Cited by 909
Self-citationIs the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose
Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14401 · Cited by 863
Opening Closed Regimes: What Was the Role of Social Media During the Arab Spring?Preprint
Howard, P. N., Duffy, A., Freelon, D., Hussain, M. M., Mari, W. T., & Maziad, M. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2595096 · Cited by 709
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. A. (2017) · National Bureau of Economic Research · doi:10.3386/w23089 · Cited by 654
Science vs Conspiracy: Collective Narratives in the Age of Misinformation
Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118093 · Cited by 645
Detecting and Tracking Political Abuse in Social Media
Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Flammini, A., & Menczer, F. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14127 · Cited by 576
The dynamics of protest recruitment through an online network
González-Bailón, S., Borge-Holthoefer, J., Rivero, A., & Moreno, Y. (2011) · Scientific Reports · doi:10.1038/srep00197 · Cited by 571
Ratkiewicz (2011) · doi:10.1145/1963192.1963301 · Cited by 541
Measuring Emotional Contagion in Social Media
Ferrara, E., & Yang, Z. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142390 · Cited by 528
Self-citationThe DARPA Twitter Bot Challenge
Subrahmanian, V. S., Azaria, A., Durst, S., Kagan, V., Galstyan, A., Lerman, K., Zhu, L., Ferrara, E., Flammini, A., & Menczer, F. (2016) · Computer · doi:10.1109/mc.2016.183 · Cited by 480
Self-citationThe Dynamics of Protest Recruitment through an Online Network
González‐Bailón, S., Borge‐Holthoefer, J., Rivero, A., & Moreno, Y. (2011) · ScholarlyCommons (University of Pennsylvania) · Cited by 466
(2014) · iConference 2014 Proceedings · doi:10.9776/14308 · Cited by 423
Bots, #Strongerin, and #Brexit: Computational Propaganda During the UK-EU Referendum
Howard, P. N., & Kollanyi, B. (2016) · doi:10.2139/ssrn.2798311 · Cited by 392
Engineering the public: Big data, surveillance and computational politics
Tufekci, Z. (2014) · First Monday · doi:10.5210/fm.v19i7.4901 · Cited by 384
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion · doi:10.1145/3041021.3055135 · Cited by 381
Integrating social media into emergency-preparedness efforts.
Merchant, R. M., Elmer, S., & Lurie, N. (2011) · New England Journal of Medicine · doi:10.1056/nejmp1103591 · Cited by 364
Evidence of complex contagion of information in social media: An experiment using Twitter bots
Mønsted, B., Sapieżyński, P., Ferrara, E., & Lehmann, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184148 · Cited by 340
Self-citationBroadcasters and hidden influentials in online protest diffusion
González-Bailón, S., Borge-Holthoefer, J., & Moreno, Y. (2013) · American Behavioral Scientist · doi:10.1177/0002764213479371 · Cited by 327
New media campaigns and the managed citizen
(2006) · Choice Reviews Online · doi:10.5860/choice.43-6172 · Cited by 324
Uncovering social network Sybils in the wild
Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B. Y., & Dai, Y. (2014) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2556609 · Cited by 290
Locally noisy autonomous agents improve global human coordination in network experiments
Shirado, H., & Christakis, N. A. (2017) · Nature · doi:10.1038/nature22332 · Cited by 288
Automating power: Social bot interference in global politics
Woolley, S. (2016) · First Monday · doi:10.5210/fm.v21i4.6161 · Cited by 281
Zipf, Power-laws, and Pareto-a ranking tutorial
Adamic, L. A. (2000) · Cited by 273
Social Media and the Elections
Metaxas, P., & Mustafaraj, E. (2012) · Science · doi:10.1126/science.1230456 · Cited by 245
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Cresci, S., Pietro, R. D., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · doi:10.48550/arxiv.1701.03017 · Cited by 237
Quantifying the effect of sentiment on information diffusion in social media
Ferrara, E., & Yang, Z. (2015) · PeerJ Computer Science · doi:10.7717/peerj-cs.26 · Cited by 233
Self-citationHow Social Media Will Change Public Health
Dredze, M. (2012) · IEEE Intelligent Systems · doi:10.1109/mis.2012.76 · Cited by 207
Combating fake news: an agenda for research and action
Lazer, D. M. J., Baum, M., Grinberg, N., Friedland, L., Joseph, K., Hobbs, W., & Mattsson, C. E. S. (2017) · Cited by 196
Uncovering social network sybils in the wild
Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B. Y., & Dai, Y. (2011) · Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference · doi:10.1145/2068816.2068841 · Cited by 164
Reverse Engineering Socialbot Infiltration Strategies in Twitter
Freitas, C. A., Benevenuto, F., Ghosh, S., & Veloso, A. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2809292 · Cited by 138
Early detection of promoted campaigns on social media
Varol, O., Ferrara, E., Menczer, F., & Flammini, A. (2017) · EPJ Data Science · doi:10.1140/epjds/s13688-017-0111-y · Cited by 133
Self-citationSocialbots: voices from the fronts
Hwang, T., Pearce, I., & Nanis, M. (2012) · INTR · doi:10.1145/2090150.2090161 · Cited by 131
Savage, S., Monroy‐Hernández, A., & Höllerer, T. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2819985 · Cited by 120
Political Bots and the Manipulation of Public Opinion in VenezuelaPreprint
Forelle, M., Howard, P. N., Monroy‐Hernández, A., & Savage, S. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2635800 · Cited by 116
Contagion dynamics of extremist propaganda in social networks
Ferrara, E. (2017) · Information Sciences · doi:10.1016/j.ins.2017.07.030 · Cited by 103
Self-citationYou followed my bot! Transforming robots into influential users in Twitter
Messias, J., Schmidt, L., Oliveira, R., & Benevenuto, F. (2013) · First Monday · doi:10.5210/fm.v18i7.4217 · Cited by 99
Political communication, computational propaganda, and autonomous agents - Introduction
Howard, P., & Woolley, S. (2016) · International journal of communication · Cited by 89
Manipulation and abuse on social mediaPreprint
Ferrara, E. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.03752 · Cited by 65
Self-citationTaking tweets to the streets: A spatial analysis of the Vinegar Protests in Brazil
Bastos, M. T., Recuero, R., & Zago, G. D. S. (2014) · First Monday · doi:10.5210/fm.v19i3.5227 · Cited by 56
On the Influence of Social Bots in Online Protests
Suárez–Serrato, P., Roberts, M. E., Davis, C. A., & Menczer, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47874-6_19 · Cited by 55
Wang, Y., Li, Y., & Luo, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14783 · Cited by 52
Gilani, Z., Wang, L., Crowcroft, J., Almeida, M., & Farahbakhsh, R. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872518.2889360 · Cited by 52
Wang, Y., Li, Y., & Luo, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.03097 · Cited by 48
Automation, Big Data and Politics: A Research Review
Shorey, S., & Howard, P. N. (2016) · International journal of communication · Cited by 42
Alternative Narratives of Crisis Events
Nied, A. C., Stewart, L. G., Spiro, E. S., & Starbird, K. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3022198.3026307 · Cited by 29
Social media as a government propaganda tool in post-revolutionary Egypt
El-Khalili, S. (2013) · First Monday · doi:10.5210/fm.v18i3.4620 · Cited by 26
Contagion Dynamics of Extremist Propaganda in Social NetworksPreprint
Ferrara, E. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2982259 · Cited by 19
Self-citationImproved Brain Pattern Recovery through Ranking Approaches
Pedregosa, F., Cauvet, É., Varoquaux, G. P., Pallier, C., Thirion, B., & Gramfort, A. (2012) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2012.23 · Cited by 4
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
Sorted by citations
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
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
The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · Nature Communications · doi:10.1038/s41467-018-06930-7 · Cited by 1,125
Tucker, J. A., Guess, A., Barbera, P., Vaccari, C., Siegel, A., Sanovich, S., Stukal, D., & Nyhan, B. (2018) · doi:10.2139/ssrn.3144139 · Cited by 1,222
Fake news game confers psychological resistance against online misinformation
Roozenbeek, J., & van der Linden, S. (2019) · Palgrave Communications · doi:10.1057/s41599-019-0279-9 · Cited by 708
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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