This paper argues that norms, not individual choices, govern how young people use social media and proposes designing an independent platform to build trusted connections.

This study investigated user reactions to inferred personal information from their own ChatGPT histories, finding that acceptability is governed by context-sensitive norms regarding generation, retent…

This paper conducted a randomized controlled trial on Reddit to test the effectiveness of various deescalation strategies in reducing personal insults using automated replies.

The paper introduces EUDAIMONIA, a new framework and benchmark for evaluating how well LLMs align with user welfare in social interactions, finding that even state-of-the-art models frequently violate…

This paper addresses the lack of specialized NLP tools for detecting toxicity in real-time video game chat by creating a large, fine-grained dataset and developing a superior, domain-specific detector…

This paper examines how ChatGPT is framed in educational YouTube videos and investigates the relationship between framings and audience response, using multimodal metadata and Epistemic Network Analys…

This study explores why and how users customize social AI, identifying motivations and contributions to a closer relationship.

This paper investigates how socially calibrated large language models distinguish when to incorporate others' perspectives from maintaining their own moral judgments, using three studies.

This paper studies how social structure influences what LLM agents express publicly versus off-the-record in debates, finding significant public-OTR divergence.

The paper proposes a hierarchical framework, PHF (Practice-Habitus-Field), inspired by Bourdieu's Theory of Practice, to improve LLM personalization by modeling user behaviors at three distinct levels…

This paper introduces IntentTune, a framework for inferring user intent from under-specified queries in e-commerce search using user-specific behavioral signals and population-level demand patterns.

The paper introduces the VET Framework, a tool for analyzing polarized public discourse on AI by categorizing narratives based on valence, effectiveness, and trajectory, thereby promoting AI literacy.

The paper proposes OneReason, a framework that enhances the reasoning capability of generative recommendation models by focusing on improving item perception and structuring user behavior into coheren…

The paper introduces CultureForest, a new benchmark for evaluating Cultural Norm Grounded Reasoning in LLMs, demonstrating that models struggle to apply their cultural knowledge effectively in realist…

The paper proposes SPHERE, a novel framework that uses large language models to create semantic user personas, enabling effective cross-domain recommendation knowledge transfer between completely disj…

This paper proposes HyCoRec, a method to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences.

Analyzing longitudinal data from 12,000 Copilot users, the paper finds that individual user habits regarding LLM interaction are highly sticky and difficult to change, and that existing datasets may o…

The paper introduces CASTER, a new human-centric task for evaluating User-Generated Content (UGC) resonance, and proposes MEDEA, an architecture that uses a Social Chain-of-Thought mechanism to simula…

The paper advocates for integrating explicit contextual feedback (like reviews and comments) into LLM-based recommender systems to achieve more personalized, transparent, and semantically aligned reco…

This paper introduces the ExpressionCueLens framework to analyze how anthropomorphism expressions are used in human-AI companion agent interactions on Reddit and XiaoHongShu, revealing cultural and pl…