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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Bots can produce credible content with human-like temporal patterns.
“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]
The deep neural network model classified 13.4% of users as bots, a value compatible to estimations during other voting events.
“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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Who Falls for Online Political Manipulation?
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Self-citationEarly Warnings of Cyber Threats in Online Discussions
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Russian propaganda on social media during the 2022 invasion of Ukraine
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Empowering political participation through artificial intelligence
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Contagion dynamics of extremist propaganda in social networks
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A new sociology of humans and machines
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Self-citationSapienza, A., Ernala, S. K., Bessi, A., Lerman, K., & Ferrara, E. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191528 · Cited by 55 · see citing works
Self-citationAl‐Rawi, A., Groshek, J., & Zhang, L. (2018) · Online Information Review · doi:10.1108/oir-02-2018-0065 · Cited by 54 · see citing works
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Influence of augmented humans in online interactions during voting events
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“…of users in our data set as “humans” or “bots” is based on features providing the best classification accuracy according to recent studies [11] Searching for better performance we tested different machine learning techniques on an independent dataset created ad-hoc (see Supplementary…”
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Ephemeral Astroturfing Attacks: The Case of Fake Twitter Trends
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Sourcing and Automation of Political News and Information During Three European Elections
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GANBOT: a GAN-based framework for social bot detection
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Measuring Bot and Human Behavioral Dynamics
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Self-citationDisinformation in Facebook Ads in the 2019 Spanish General Election Campaigns
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Bot detection using a single post on social media
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Who Let The Trolls Out? Towards Understanding State-Sponsored TrollsPreprint
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Traffic networks are vulnerable to disinformation attacks
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Detecting Bots and Assessing Their Impact in Social Networks
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- 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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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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