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Cited by 9Misinformation and Its ImpactsSpam and Phishing DetectionSocial Media and Politics

Dormant bots in social media: Twitter and the 2018 U.S. senate election

Takacs, Richard, McCulloh, Ian·2019·Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
DOI 10.1145/3341161.3343852
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Abstract

Bots are often identified on social media due to their behavior. How easily are they identified, however, when they are dormant and exhibit no measurable behavior at all, except for their silence? We identified "dormant bot networks" positioned to influence social media discourse surrounding the 2018 U.S. senate election. A dormant bot is a social media persona that does not post content yet has large follower and friend relationships with other users. These relationships may be used to manipulate online narratives and elevate or suppress certain discussions in the social media feed of users. Using a simple structure-based approach, we identify a large number of dormant bots created in 2017 that begin following the social media accounts of numerous US government politicians running for re-election in 2018. Findings from this research were used by the U.S. Government to suspend dormant bots prior to the elections to prevent any malign influence campaign. Application of this approach by social media providers may provide a novel method to reduce the risk of content manipulation for online platforms.

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Crossref · 10.1145/3341161.3343852

Cites

19 of 19 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 41% of the Twitter accounts following US Senators before the 2018 elections never posted any tweets.

    “Another study showed that 47% of all Twitter accounts following US Senators before the 2018 elections were created within six months in 2017; 41% percent of these accounts never posted any tweets and had an average of 1.7 followers and 99 friends [41].”

    From Unsupervised detection of coordinated fake-follower campaigns on social media (2024) · cites this paper as [41]

  • 47% of all Twitter accounts following US Senators before the 2018 elections were created within six months in 2017.

    “Another study showed that 47% of all Twitter accounts following US Senators before the 2018 elections were created within six months in 2017; 41% percent of these accounts never posted any tweets and had an average of 1.7 followers and 99 friends [41].”

    From Unsupervised detection of coordinated fake-follower campaigns on social media (2024) · cites this paper as [41]

  • Twitter accounts following US Senators before the 2018 elections had an average of 1.7 followers and 99 friends.

    “Another study showed that 47% of all Twitter accounts following US Senators before the 2018 elections were created within six months in 2017; 41% percent of these accounts never posted any tweets and had an average of 1.7 followers and 99 friends [41].”

    From Unsupervised detection of coordinated fake-follower campaigns on social media (2024) · cites this paper as [41]

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Authors
Takacs, R., & McCulloh, I.
Affiliations
  • Johns Hopkins University (US)
  • Accenture (Switzerland) (CH)
ORCID
McCulloh, Ian
Venue
Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Publisher
ACM
Published
2019-08-27
Pages
796-800
DOI
10.1145/3341161.3343852
OpenAlex
W2999470208
Cited by
9
References
19
Citations by year
2025: 3 · 2024: 2 · 2023: 3 · 2022: 1
FWCI
0.71
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (publishedVersion)
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
social bots, botnets, social media personas, bot detection

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