Tai-Quan Peng

Polarizing language is a key mechanism through which intergroup boundaries are constructed and experienced, yet research has rarely specified how distinct linguistic strategies contribute to perceived polarization. This article develops and validates a coding instrument that conceptualizes polarizing language as a communicative process. Drawing on social identity, facework, and persuasion theories, we identify eight functionally distinct language strategies that intensify intergroup contrast or dampen counter-arguing. Across four studies, we link language use to audience perception and examine the generalizability of the framework. Study 1 applies the coding scheme to U.S. governors’ speeches. Study 2 examines audience perceptions of each strategy. Study 3 constructs a perception-weighted index integrating prevalence and perceived impact. Study 4 extends validation to congressional tweets, demonstrating that the categories are empirically distinguishable and applicable across communicative contexts using both manual and computational approaches. The approach advances the psychological measurement of polarizing discourse.

As large language models (LLMs) become increasingly integrated into everyday life, their influence on how people access, interpret, and engage with political information is growing rapidly. From as...

This study attempts to advance automated content analysis from consensus-oriented to coordination-oriented practices, thereby embracing diverse coding outputs and exploring the dynamics among differential perspectives. As an exploratory investigation, we evaluate six GPT-4o configurations to analyze sentiment in Fox News and MSNBC transcripts on Biden and Trump during the 2020 U.S. presidential campaign. By assessing each model’s alignment with partisan perspectives, we explore how partisan selective processing can be identified in LLM-Assisted Content Analysis (LACA). The findings indicate that LLM-based partisan perspective simulations reflect politically polarized standpoints across partisan groups, revealing a pronounced divergence in sentiment analysis between Democrat-aligned and Republican-aligned persona models. This pattern is evident in intercoder-reliability metrics, which are higher among same-partisan than cross-partisan persona model pairs. Results also suggest that LLM partisan simulations exhibit stronger ideological biases when analyzing politically congruent content. This approach enhances the nuanced understanding of LLM outputs and advances the integrity of AI-driven social science research and may also enable simulations of real-world implications.

Sentiment contagion occurs when attitudes toward one topic are influenced by attitudes toward others. Detecting and understanding this phenomenon is essential for analyzing topic evolution and informing social policies. Prior research has developed models to simulate the contagion process through hypothesis testing and has visualized user–topic correlations to aid comprehension. Nevertheless, the vast volume of topics and the complex interrelationships on social media present two key challenges: (1) efficient construction of large-scale sentiment contagion networks, and (2) in-depth explorations of these networks. To address these challenges, we introduce a causality-based framework that efficiently constructs and explains sentiment contagion. We further propose a map-like visualization technique that encodes time using a horizontal axis, enabling efficient visualization of causality-based sentiment flow while maintaining scalability through limitless spatial segmentation. Based on the visualization, we develop CausalMap, a system that supports analysts in tracing sentiment contagion pathways and assessing the influence of different demographic groups. Furthermore, we conduct comprehensive evaluations——including two use cases, a task-based user study, an expert interview, and an algorithm evaluation——to validate the usability and effectiveness of our approach.

Mental health is an urgent societal issue, and social scientists are increasingly turning to online mental health communities (OMHCs) to analyze user behavior data for early intervention. However, existing sequence mining techniques fall short of the urgent need to explore the behavior progression of different groups (e.g., recovery or deterioration groups) and track the potential long-term impact of behaviors on mental health status. To address this issue, we introduce EMINDS, a visual analytics system built on a novel automatic mining pipeline that extracts distinct behavior stages and assesses the potential impact of frequent stage patterns on mental health status over time. The system includes a set of interactive visualizations that summarize the meaning of each behavior stage and the evolution of different stage patterns. We feature a pattern-centric Sankey to reveal contextual information about the impact of stage patterns on mental health, helping experts understand the specific changes in sequences before and after a stage pattern. We evaluated the effectiveness and usability of EMINDS through two case studies and expert interviews, which examined the potential stage patterns impacting long-term mental health by analyzing user behaviors on Reddit.

As Human Communication Research begins a new editorial chapter, this editorial reaffirms the journal’s longstanding commitment to theory-driven scholarship while addressing recurring challenges that shape how communication research is conceptualized, designed, and interpreted. Drawing from editorial experience and field-wide observations, I identify ten patterns—such as sidelining theory, prioritizing statistical effects over explanatory mechanisms, and equating analytical complexity with conceptual rigor—that signal misalignments between empirical practice and theoretical ambition. These challenges are mapped onto Chaffee and Berger’s classic evaluative criteria to clarify where theoretical contributions often falter. In response, I outline an editorial vision centered on conceptual coherence, methodological alignment, and integrative theorizing. The editorial also introduces Commentaries and Catalysts, a new column designed to foreground theoretical insight and foster cross-paradigm dialogue. Together, these reflections aim to support authors and reviewers while inviting engagement with the standards, possibilities, and evolving responsibilities of cumulative theory-building in communication research.