Characterizing the role of bots’ in polarized stance on social media

• Published in: Social network analysis and mining Vol. 12; no. 1; p. 30
• Main Authors: Aldayel, Abeer, Magdy, Walid
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01-12-2022; Springer Nature B.V
• Subjects: Accounts; Applications of Graph Theory and Complex Networks; Automation; Computer mediated communication; Computer Science; Data Mining and Knowledge Discovery; Digital media; Economics; False information; Game Theory; Humanities; Hypotheses; Law; Methodology of the Social Sciences; Original; Original Article; Social and Behav. Sciences; Social media; Social networks; Software agents; Statistics for Social Sciences; United States > US; Stance; Social media; Bots
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-022-00858-z

There is a rising concern with social bots that imitate humans and manipulate opinions on social media. Current studies on assessing the overall effect of bots on social media users mainly focus on evaluating the diffusion of discussions on social networks by bots. Yet, these studies do not confirm the relationship between bots and users’ stances. This study fills in the gap by analyzing if these bots are part of the signals that formulated social media users’ stances towards controversial topics. We analyze users’ online interactions that are predictive to their stances and identify the bots within these interactions. We applied our analysis on a dataset of more than 4000 Twitter users who expressed a stance on seven different topics. We analyzed those users’ direct interactions and indirect exposures with more than 19 million accounts. We identify the bot accounts for supporting/against stances, and compare them to other types of accounts, such as the accounts of influential and famous users. Our analysis showed that bot interactions with users who had specific stances were minimal when compared to the influential accounts. Nevertheless, we found that the presence of bots was still connected to users’ stances, especially in an indirect manner, as users are exposed to the content of the bots they follow, rather than by directly interacting with them by retweeting, mentioning, or replying.