How social bots can influence public opinion more effectively: Right connection strategy

Yaozeng Zhang, Jing Ma and Fanshu Fang

Physica A: Statistical Mechanics and its Applications, 2024, vol. 633, issue C

Abstract: The advent of online social networks (OSNs) has facilitated the exchange of opinions and information, but the prevalence of social bots that manipulate opinions through human-like behavior demands attention. In response, we present a novel approach that utilizes a virtual corpus to analyze the impact of social bots on public opinion in OSNs. By developing behavior rules for both bots and human users, we aim to identify optimal strategies for social bots to increase their effectiveness. This work reveals that the success of social bots on OSNs depends on their connectivity strategy to corresponding OSN topologies. We found that simply increasing the number of social bots is not always effective, and dense communication links in target OSNs can dilute the impact of information posted by social bots. As a result, we explored other factors, such as the walking speed of bots in OSNs, to provide rules that inspire the development of smarter and more effective social bots. Our research sheds light on the intricate dynamics between social bots and human users in OSNs and provides valuable insights into social bots’ behaviors, informing effective strategies for their design and deployment.

Keywords: Public opinion formation; Social bots; Agent interactions; Network analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:633:y:2024:i:c:s037843712300941x

DOI: 10.1016/j.physa.2023.129386

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