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Discovery and classification of Twitter bots

Shevtsov Alexander, Oikonomidou Maria, Antonakaki Despoina, Pratikakis Polyvios, Kanterakis Alexandros, Fragopoulou, Paraskevi, Ioannidis Sotirios

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Year 2022
Type of Item Peer-Reviewed Journal Publication
Bibliographic Citation A. Shevtsov, M. Oikonomidou, D. Antonakaki, P. Pratikakis, A. Kanterakis, P. Fragopoulou and S. Ioannidis, “Discovery and classification of Twitter bots,” SN Comput. Sci., vol. 3, no. 3, Apr. 2022, doi: 10.1007/s42979-022-01154-5.
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Online social networks (OSN) are used by millions of users, daily. This user-base shares and discovers different opinions on popular topics. The social influence of large groups may be affected by user beliefs or be attracted by the interest in particular news or products. A large number of users, gathered in a single group or number of followers, increases the probability to influence more OSN users. Botnets, collections of automated accounts controlled by a single agent, are a common mechanism for exerting maximum influence. Botnets may be used to better infiltrate the social graph over time and create an illusion of community behaviour, amplifying their message and increasing persuasion. This paper investigates Twitter botnets, their behavior, their interaction with user communities, and their evolution over time. We analyze a dense crawl of a subset of Twitter traffic, amounting to nearly all interactions by Greek-speaking Twitter users for a period of 36 months. The collected users are labeled as botnets, based on long-term and frequent content similarity events. We detect over a million events, where seemingly unrelated accounts tweeted nearly identical content, at almost the same time. We filter these concurrent content injection events and detect a set of 1850 accounts that repeatedly exhibit this pattern of behavior, suggesting that they are fully or in part controlled and orchestrated by the same entity. We find botnets that appear for brief intervals and disappear, as well as botnets that evolve and grow, spanning the duration of our dataset. We analyze the statistical differences between the bot accounts and the human users, as well as the botnet interactions with the user communities and the Twitter trending topics.