social media product teams

Abstract: Our mixed-method study examines how Chinese women livestreamers use masking to construct idealized mediated personas while navigating gendered, commercial, organizational, and platform pressures alongside personal agendas. We built on the concept of masking, analyzed 627 recruitment posts, and conducted livestream observations and interviews with 26 Chinese women streamers. We found that streamers assembled masks across bodies, AI-mediated technologies, spaces, performances, and social relations to become recognizable while protecting personal boundaries. These masks were continually negotiated, and participants sometimes broke, resisted, or refused them when demands became misaligned or unsustainable. We conceptualize masking as a sociotechnical assemblage in which agency lies in preserving, disrupting, and reconfiguring relations rather than controlling a single interface. We further theorize breaking as a consequential part of masking that exposes hidden labor and unequal costs of visibility. We offer theoretical and design directions for more negotiable, contestable, and agency-supporting AI-mediated self- presentation.

Abstract: User behavior in real-world recommender systems is heterogeneous. While some users exhibit coherent preferences, others show abrupt interest shifts, bursty interactions, excessive repetition, or inconsistency with collaborative neighborhoods. Such deviations may arise from benign variation or manipulation, including shilling attacks, but do not alone establish malicious intent. Existing robust recommenders exploit user-risk signals through training-time reweighting or graph aggregation, whereas adapting generation to estimated user-level weak risk remains underexplored in LLM-based generative recommendation. We propose ReliGRec (Reliability-oriented Generative Recommendation), a weakly supervised framework whose name denotes its design goal rather than a supervised reliability variable. ReliGRec derives user-level weak-risk proxy labels from review-feedback signals for a subset of users and represents sequential behavior and collaborative context using a Behavior Token and temporal Graph Tokens, respectively. A Dual-View Weak-Risk Estimator fuses the representations to produce a user-level weak-risk score that selects a Simple or Cautious Prompt at inference. The Cautious Prompt is designed to encourage attention to stable, collaboratively supported evidence while reducing overreliance on isolated, short-term, or repeated interactions. The Behavior Token affects generation through weak-risk estimation and routing, whereas the aggregated Graph Token provides collaborative context for next-item Semantic ID generation. ReliGRec thus turns weak-risk estimation from an auxiliary prediction into a generation-time control signal. Experiments report competitive recommendation and weak-risk proxy-label prediction, while routing analyses characterize the recommendation-quality and inference-cost behavior of weak-risk-guided prompting.