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Teens often encounter cyberbullying on social media. One promising way to reduce cyberbullying is through empowering teens to stand up for their peers and cultivating prosocial norms online. While there is no shortage of bystander interventions that have shown potential, little research has explored designing chatbots with users to provide a contextualized and embedded “learning at the moment” experience for bystanders. This study involved teens and educators in two design sessions: an in-depth interview to identify the barriers that prevent upstanding behaviors, and interaction with the “social media co-pilot'' chatbot prototype to identify design guidelines to empower teens to overcome these barriers. Qualitative analysis on the conversations from the two design sessions revealed three factors that curb teens' upstanding behaviors: a) inadequate knowledge about social norms, appropriate language, and consequences, b) inhibitive emotions such as fear of retaliation and confrontation; c) lack of empathy toward their peers. Key parameters were also identified to shape chatbot responses to encourage upstanding behaviors, such as a) adopting voices representing multiple roles, b) empathetic, friendly and encouraging tone, c) reflective, specific and relatable language and d) appropriate length. These insights inform the design of personalized and scalable education programs and moderation tools to combat cyberbullying. 

Abstract Problematic content on social media can be countered through objections raised by other community members. While intended to deter offenses, objections can influence the surrounding audience observing the interaction, leading to their collective approval or disapproval. The results of an experiment manipulating seven types of objections against common types of offenses indicate audiences’ support for objections that implore via appeals and disapproval of objections that threaten the offender, as they view the former as more moral, appropriate, and effective compared to the latter. Furthermore, audiences tend to prefer more benign and less threatening objections regardless of the offense severity (following the principle of “taking the high road”) instead of objections proportionate to the offense (“an eye for an eye”). Taken together, these results show how objections to offensive behaviors may impact collective perceptions on social media, paving the way for interventions to foster effective objection strategies in social media discussions. 

Abstract Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI’s negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions (“smart replies”), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly. 

As AI-mediated communication (AI-MC) becomes more prevalent in everyday interactions, it becomes increasingly important to develop a rigorous understanding of its effects on interpersonal relationships and on society at large. Controlled experimental studies offer a key means of developing such an understanding, but various complexities make it difficult for experimental AI-MC research to simultaneously achieve the criteria of experimental realism, experimental control, and scalability. After outlining these methodological challenges, this paper offers the concept of methodological middle spaces as a means to address these challenges. This concept suggests that the key to simultaneously achieving all three of these criteria is to abandon the perfect attainment of any single criterion. This concept's utility is demonstrated via its use to guide the design of a platform for conducting text-based AI-MC experiments. Through a series of three example studies, the paper illustrates how the concept of methodological middle spaces can inform the design of specific experimental methods. Doing so enabled these studies to examine research questions that would have been either difficult or impossible to investigate using existing approaches. The paper concludes by describing how future research could similarly apply the concept of methodological middle spaces to expand methodological possibilities for AI-MC research in ways that enable contributions not currently possible.