Automated Diffusion? Bots and Their Influence During the 2016 U.S. Presidential Election

Boichak, Olga; Jackson, Sam; Hemsley, Jeff; Tanupabrungsun, Sikana

doi:10.1007/978-3-319-78105-1_3

Cited by 25 publications

(26 citation statements)

References 13 publications

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“…Moreover, since we combine liberal and liberal-center into one list (same for conservatives), we can see that the algorithm is not only labeling the far liberal or conservative correctly, which is a relatively easier task, but it is performing well on the liberal/conservative center as well. 4 https://mediabiasfactcheck.com/…”

Section: Political Ideology Inferencementioning

confidence: 99%

Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior

Luceri

Deb

Badawy

et al. 2019

Companion Proceedings of the 2019 World Wide Web Conference

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Recent research brought awareness of the issue of bots on social media and the significant risks of mass manipulation of public opinion in the context of political discussion. In this work, we leverage Twitter to study the discourse during the 2018 US midterm elections and analyze social bot activity and interactions with humans. We collected 2.6 million tweets for 42 days around the election day from nearly 1 million users. We use the collected tweets to answer three research questions: (i) Do social bots lean and behave according to a political ideology? (ii) Can we observe different strategies among liberal and conservative bots? (iii) How effective are bot strategies?We show that social bots can be accurately classified according to their political leaning and behave accordingly. Conservative bots share most of the topics of discussion with their human counterparts, while liberal bots show less overlap and a more inflammatory attitude. We studied bot interactions with humans and observed different strategies. Finally, we measured bots embeddedness in the social network and the effectiveness of their activities. Results show that conservative bots are more deeply embedded in the social network and more effective than liberal bots at exerting influence on humans.

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Section: Political Ideology Inferencementioning

confidence: 99%

Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior

Luceri

Deb

Badawy

et al. 2019

Companion Proceedings of the 2019 World Wide Web Conference

View full text Add to dashboard Cite

show abstract

“…We classify users by their ideology based on the political leaning of the media outlets they share. We use lists of partisan media outlets compiled by third-party organizations, such as AllSides 8 and Media Bias/Fact Check. 9 We combine liberal and liberal-center media outlets into one list and conservative and conservative-center into another.…”

Section: Political Ideology Inferencementioning

confidence: 99%

Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms

Deb¹,

Luceri

Badaway³

et al. 2019

Companion Proceedings of the 2019 World Wide Web Conference

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One of the hallmarks of a free and fair society is the ability to conduct a peaceful and seamless transfer of power from one leader to another. Democratically, this is measured in a citizen population's trust in the electoral system of choosing a representative government. In view of the well documented issues of the 2016 US Presidential election, we conducted an in-depth analysis of the 2018 US Midterm elections looking specifically for voter fraud or suppression. The Midterm election occurs in the middle of a 4 year presidential term. For the 2018 midterms, 35 Senators and all the 435 seats in the House of Representatives were up for re-election, thus, every congressional district and practically every state had a federal election. In order to collect election related tweets, we analyzed Twitter during the month prior to, and the two weeks following, the November 6, 2018 election day. In a targeted analysis to detect statistical anomalies or election interference, we identified several biases that can lead to wrong conclusions. Specifically, we looked for divergence between actual voting outcomes and instances of the #ivoted hashtag on the election day. This analysis highlighted three states of concern: New York, California, and Texas. We repeated our analysis discarding malicious accounts, such as social bots. Upon further inspection and against a backdrop of collected general election-related tweets, we identified some confounding factors, such as population bias, or bot and political ideology inference, that can lead to false conclusions. We conclude by providing an in-depth discussion of the perils and challenges of using social media data to explore questions about election manipulation.

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“…In particular, bots, due to their scalable nature, represent a major concern in the fight against media manipulation (Varol et al , 2017b;Monsted et al , 2017;Boichak et al , 2018;Shao et al , 2018;Yang et al , 2019)…”

Section: Introductionmentioning

confidence: 99%

Evolution of bot and human behavior during elections

Luceri¹,

Deb²,

Giordano³

et al. 2019

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Online social media have become one of the main communication platforms for political discussion. The online ecosystem, however, does not only include human users but has given a space to an increasing number of automated accounts, referred to as bots, extensively used to spread messages and manipulate the narratives others are exposed to. Although social media service providers put increasing efforts to protect their platforms, malicious bot accounts continuously evolve to escape detection. In this work, we monitored the activity of almost 245K accounts engaged in the Twitter political discussion during the last two U.S. voting events. We identified approximately 31K bots and characterized their activity in contrast with humans. We show that, in the 2018 midterms, bots changed the volume and the temporal dynamics of their online activity to better mimic humans and avoid detection. Our findings highlight the mutable nature of bots and illustrate the challenges to forecast their evolution.

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Automated Diffusion? Bots and Their Influence During the 2016 U.S. Presidential Election

Cited by 25 publications

References 13 publications

Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior

Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior

Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms

Evolution of bot and human behavior during elections

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