This research examines how TikTok users conceptualize and engage with personalized algorithms on the TikTok platform. Using qualitative methods, we analyzed 24 interviews with TikTok users to explore how algorithmic personalization processes inform people's understanding of their identities as well as shape their orientation to others. Building on insights from our qualitative data and previous scholarship on algorithms and identity, we propose a novel conceptual model to understand how people think about and interact with personalized algorithmic systems. Drawing on the metaphor of crystals and their properties, the algorithmic crystal framework is an analytic frame that captures user understandings of how personalized algorithms (1) interact with user identity by reflecting user self-concepts that are both multifaceted and dynamic and (2) shape perspectives on others encountered through the algorithm, by orienting users to recognize parts of themselves refracted in other users and to experience ephemeral, diffracted connections with groups of similar others. We describe how the algorithmic crystal framework can extend theory and inform new lines of research around the implications of algorithms in self-concept development and social life.

AI-Mediated Communication (AI-MC) is interpersonal communication that involves an artificially intelligent system that can modify, augment, or even generate content to achieve communicative and relational goals. AI-MC is increasingly involved in human communication and has the potential to impact core aspects of human communication, such as language production, interpersonal perception and task performance. Through a between-subjects experimental design we examine how these processes are influenced when integrating AI-generated language in the form of suggested text responses (Google's smart replies) into a text-based referential communication task. Our study replicates and extends the impacts of a positivity bias in AI-generated language and introduces the adjacency pair framework into the study of AI-MC. We also find preliminary yet mixed evidence to suggest that AI-generated language has the potential to undermine some dimensions of interpersonal perception, such as social attraction. This study contributes important concepts for future work in AI-MC and offers findings with implications for the design of AI systems in human-to-human communication.

This paper examines relationships between perceptions of warmth and competence, emotional responses, and behavioral tendencies in the context of social robots. Participants answered questions about these three aspects of impression formation after viewing an image of one of 342 social robots in the Stanford Social Robots Database. Results suggest that people have similar emotional and behavioral reactions to robots as they have to humans; impressions of the robots' warmth and competence predicted specific emotional responses (admiration, envy, contempt, pity) and those emotional responses predicted distinct behavioral tendencies (active facilitation, active harm, passive facilitation, passive harm). However, the predicted relationships between impressions and harmful behavioral tendencies were absent. This novel asymmetry for perceptions and intentions towards robots is deliberated in the context of the computers as social actors framework and opportunities for further research are discussed.