To promote the future design of CA systems, we conducted a literature review of ACM publications and identified a set of works that conducted UX (user experience) research. However, research on polyadic CAs is scattered across different fields, making it challenging to identify, compare, and accumulate existing knowledge. CAs for polyadic interactions are unique because they encompass hybrid social interactions, i.e., human-CA, human-to-human, and human-to-group behaviors. Further issues of engagement in these contexts include uneven participation, in which a balanced amount of contribution is hard to achieve [Kim et al., 2020, Shamekhi et al., 2018, and user attractions are challenging to capture in casual social settings [Otogi et al., 2013, Zheng et al., 2005.Įarly conversational agents (CAs) focused on dyadic human-AI interaction between humans and the CAs, followed by the increasing popularity of polyadic human-AI interaction, in which CAs are designed to mediate human-human interactions. For example, in peer learning settings, it is challenging to engage students in provocative discussions, to promote productive talk among students, such as explaining to peers and re-voicing others' statements and greater community activity are needed [Seering et al., 2018, 2020, Luria et al., 2020a, Abokhodair et al., 2015, Wang et al., 2021. 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. Our analysis showed that bot interactions with users who had specific stances were minimal when compared to the influential 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. We analyzed those users’ direct interactions and indirect exposures with more than 19 million accounts. We applied our analysis on a dataset of more than 4000 Twitter users who expressed a stance on seven different topics. We analyze users’ online interactions that are predictive to their stances and identify the bots within these interactions. 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.

Yet, these studies do not confirm the relationship between bots and users’ stances. 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. There is a rising concern with social bots that imitate humans and manipulate opinions on social media.

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