https://doi.org/10.1140/epjds/s13688-024-00499-6
Research
Unsupervised detection of coordinated fake-follower campaigns on social media
1
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
2
Center of Excellence in Data Analytics, Sabanci University, Istanbul, Turkey
Received:
6
February
2024
Accepted:
26
September
2024
Published online:
7
October
2024
Automated social media accounts, known as bots, are increasingly recognized as key tools for manipulative online activities. These activities can stem from coordination among several accounts and these automated campaigns can manipulate social network structure by following other accounts, amplifying their content, and posting messages to spam online discourse. In this study, we present a novel unsupervised detection method designed to target a specific category of malicious accounts designed to manipulate user metrics such as online popularity. Our framework identifies anomalous following patterns among all the followers of a social media account. Through the analysis of a large number of accounts on the Twitter platform (rebranded as X after the acquisition of Elon Musk), we demonstrated that irregular following patterns are prevalent and are indicative of automated fake accounts. Notably, we found that these detected groups of anomalous followers exhibited consistent behavior across multiple accounts. This observation, combined with the computational efficiency of our proposed approach, makes it a valuable tool for investigating large-scale coordinated manipulation campaigns on social media platforms.
Key words: Computational social science / Fake-followers / Bots / Online coordinated activities / Misinformation
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-024-00499-6.
© The Author(s) 2024
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