https://doi.org/10.1140/epjds/s13688-023-00406-5
Regular Article
Emergent local structures in an ecosystem of social bots and humans on Twitter
1
Department of Network and Data Science, Central European University, Quellenstraße, 1100, Vienna, Austria
2
Observatory on Social Media, Indiana University, Bloomington, IN, USA
3
Complexity Science Hub, Vienna, Austria
Received:
2
April
2023
Accepted:
19
July
2023
Published online:
22
September
2023
Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patterns in online social networks, and can be used to develop more effective bots for beneficial tasks and to combat malicious actors.
Key words: Social bots / Bot-human ecosystem / Bot strategies / Network motifs
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-023-00406-5.
© The Author(s) 2023
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