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Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election

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arxiv 1707.00086 v1 pith:QKJKW7AF submitted 2017-07-01 cs.SI cs.HCphysics.soc-ph

classification cs.SIcs.HCphysics.soc-ph
keywords disinformationusersbotselectionfrenchmediasocialaccounts
verification ladder T0 review T1 audit T2 compute T3 formal
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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 occurred in the run up to the 2017 French presidential election. We collected a massive Twitter dataset of nearly 17 million posts occurred between April 27 and May 7, 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 taken 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 the scarce success of this campaign: the users who engaged with MacronLeaks are mostly foreigners with a preexisting 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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Cited by 2 Pith papers

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  1. Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery

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    BotHP combines a dual-encoder (graph and MLP) with prototype-guided clustering to pre-train graph bot detectors, improving F1 by 1.3-6.0 points on TwiBot-20 and MGTAB.

  2. Graph-based Fake Account Detection: A Survey

    cs.SI 2025-07 conditional novelty 4.0 of 10

    A structured survey of graph-based fake account detection methods, organizing classical, traditional machine learning, and deep learning approaches and their datasets.

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