Social bots distort the 2016 U.S. Presidential election online discussion

By Alessandro Bessi, Emilio Ferrara. First Monday, 2016

986 citations recorded by the source corpus.

Social media have been extensively praised for increasing democratic discussion on social issues related to policy and politics. However, what happens when this powerful communication tools are exploited to manipulate online discussion, to change the public perception of political entities, or even to try affecting the outcome of political elections? In this study we investigated how the presence of social media bots, algorithmically driven entities that on the surface appear as legitimate users, affect political discussion around the 2016 U.S. Presidential election. By leveraging state-of-the-art social bot detection algorithms, we uncovered a large fraction of user population that may not be human, accounting for a significant portion of generated content (about one-fifth of the entire conversation). We inferred political partisanships from hashtag adoption, for both humans and bots, and studied spatio-temporal communication, political support dynamics, and influence mechanisms by discovering the level of network embeddedness of the bots. Our findings suggest that the presence of social media bots can indeed negatively affect democratic political discussion rather than improving it, which in turn can potentially alter public opinion and endanger the integrity of the Presidential election.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/42d357a8d3db09d465b71c4661750620bcf70a26

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Citations in the corpus

Combating Fake News (2019, neutral): “Malicious individuals and organizations with a political or social agenda often pose as ordinary social media users using social bots [6] or actual accounts and can act as the source as well as promoters of fake news.”
Fake news on Twitter during the 2016 U.S. presidential election (2019, positive): “Bot and troll accounts—controlled by algorithms or malicious impersonators, respectively—play welldocumented roles in manipulating political discourse (21, 28, 29).”
Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign (2018, neutral): “These effects were later documented by several studies [9, 15, 23, 25, 27, 46, 50, 56].”
The Brexit Botnet and User-Generated Hyperpartisan News (2019, positive): “Indeed, other research has described bot activity levels as incessant which on Twitter, specifically, translated into “excessive amounts of tweets” (Bessi & Ferrara, 2016).”
Fake news, disinformation and misinformation in social media: a review (2023, neutral): “Several authors (Shu et al. 2018b, 2017; Shi et al. 2019; Bessi and Ferrara 2016; Shao et al. 2018a) have also shown that fake news is likely to be created and spread by non-human accounts with similar attributes and structure in the network, such as social bots (Ferrara et al. 2016).”
Disinformation and social bot operations in the run up to the 2017 French presidential election (2017, neutral): “Recent literature reported extensively on traditional types of social bots whose automated activity can be easily detected because it yields very high volumes of tweets, especially retweets, due to incessant posting and re-sharing operations (Ferrara, et al., 2016; Bessi & Ferrara, 2016).”
No . 2017 . 5 Computational Propaganda in the United States of America : Manufacturing Consensus Online (2017, positive): “Research from several sources suggests that political bot usage was at an all-time high during key moments of this particular election (Bessi & Ferrara, 2016; Howard et al., 2016; Ferrara et al., 2016).”
Conversational AI: Social and Ethical Considerations (2019, neutral): “In a study that examined the impact of bots in the 2016 US presidential election, Bessi and Ferrara (2016) [4] found that the presence of social media bots negatively aﬀects democratic political discussion.”
False Information on Web and Social Media: A Survey (2018, neutral): “ [79], Yao et al. [114] Qazvinian et al. [77], Rubin et al. [80], Silverman et al. [91], Wang et al. [107], Yang et al. [112], Zeng et al. [118] User Hooi et al. [36], Kumar et al. [48], Bessi et al. [13], Davis et al. [21], Gupta et al. [32] Li et al. [51], Minnich et al. [58], Jin et al. [41], Kum”
Finding and Characterizing Information Warfare Campaigns (2020, neutral): “4.3.3 Internet Research Agency Data The second data set consists of tweets produced by Russia’s Internet Research Agency around the time of the 2016 US Elections and released by Twitter in October 2018 [237].”
Deep Learning for Text Style Transfer: A Survey (2020, positive): “For example, social bots (i.e., autonomous bots on social media, such as Twitter bots and Facebook bots) are a big problem in the United States, even playing a significant role in the 2016 U.S. presidential election (Bessi and Ferrara 2016; Shao et al. 2018).”
Limited individual attention and online virality of low-quality information (2017, positive): “Currently, bot accounts controlled by software make up a significant portion of online profiles [60], and many of them flood social media with high volumes of low-quality information to manipulate public discourse [21, 5].”
Deep Neural Networks for Bot Detection (2018, positive): “, social bots, or sybil accounts) have been found guilty of polluting social media conversations in a variety of scenarios. Reports of online manipulation mediated by bots span political conversation [6, 21, 31, 40, 59], fake news [19, 50], conspiracy theories [53], stock market manipulation [18], p”
Artemis: tight convergence guarantees for bidirectional compression in Federated Learning (2020, neutral): “As mentioned before, the ﬁnal impact of personalized models is also application-dependent: from medical applications to targeted ads on social media (for example, it could even be used to manipulate voters to overbalance an election, see e.g. [6]).”
Online Human-Bot Interactions: Detection, Estimation, and Characterization (2017, neutral): “Features extracted from user meta-data have been used to classify users and patterns before (Mislove et al. 2011; Ferrara et al. 2016a).”
Fake News Detection on Social Media: A Data Mining Perspective (2017, positive): “ the purpose to do harm, such as manipulating and spreading fake news on social media. Studies shows that social bots distorted the 2016 U.S. presidential election online discussions on a large scale [6], and that around 19 million bot accounts tweeted in support of either Trump or Clinton in the we”