Disinformation and social bot operations in the run up to the 2017 French presidential election

By Emilio Ferrara. First Monday, 2017

495 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/7a6823d16a1dc680c9bdfe03dc4caccbe2e944b6

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Citations in the corpus

Arming the public with artificial intelligence to counter social bots (2019, background): “Our analysis highlighted the presence of Twitter bots during the 2017 French Presidential Election (Ferrara, 2017).”
The science of fake news (2018, background): “They were responsible for a significant portion of political content posted during the 2016 campaign, and some of the same bots later attempted to influence the 2017 French election (9) .”
Characterizing the 2016 Russian IRA influence campaign (2018, background): “These effects are documented by several studies Bessi and Ferrara (2016); Conover et al. (2011a); El-Khalili (2013); Ferrara (2017); Fourney et al. (2017); Ratkiewicz et al. (2011a); Shorey and Howard (2016); Woolley and Howard (2016).”
Scalable and Generalizable Social Bot Detection through Data Selection (2019, methodology): “We can give up most of this contextual information in exchange for speed, and rely on just user metadata (Ferrara 2017; Stella, Ferrara, and De Domenico 2018).”
Finding and Characterizing Information Warfare Campaigns (2020, background): “Researchers have looked at disinformation in the 2012 Korean Presidential Elections [137], 2016 US Elections [39, 193, 265], the French National Elections [92], the Brexit Vote [130] as well as other events.”
Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign (2018, background): “Prior to this work, only a handful of other operations were linked to some specific actors [56], e.g., the alt-right attempt to smear a presidential candidate before the 2017 French election [25].”
Deep Neural Networks for Bot Detection (2018, methodology): “Though many established techniques use a large number of features ([15], for example uses over 1,500 features), recent research [19, 20] shows that similar high performance can be obtained by using a minimal number of features.”
A Survey of Fake News (2020, background): “It has been suggested that millions of social bots have participated in on-line discussions around the 2016 U.S. presidential election, 20 and some of the same bots were later used to attempt to influence the 2017 French election [Ferrara 2017].”
Bots increase exposure to negative and inflammatory content in online social systems (2018, unknown): “Here, we leveraged a scalable and accurate feature-based approach (33).”