CORTEXA
← Browse
arxivcs.HC2026-07-22

Associations Between Support-Seekers' Cross-Community Interactions and Their Engagement with Received Comments in Online Health Communities

Shenghan Tan, Daiang Jia, Chunghiu Kong, Tianjian Liu, Zhenhui Peng

Support-seekers' active engagement with received comments, e.g., showing positive sentiment and willingness to improve in the replies, can indicate the success of online health communities (OHCs). Their participation in other communities may correlate with their engagement in OHCs but remains under-explored. This paper analyzes 26, 725 seekers' behaviors in the other 40, 479 communities and their associations with seekers' engagement with received comments under their 78, 501 posts in 30 Baidu Tieba OHCs. We found that seekers primarily posted in other communities that are also health-related (25.3%), followed by those about games and entertainment (e.g., Dota, 20.8%). Seekers who posted in other communities about health (26.3%) or personal issues (e.g., saving money, 20.7%) before had relatively higher probabilities of subsequently posting in the 30 OHCs we identified, but this posting experience was associated with fewer replies and less expressed willingness to improve based on received comments. We provide insights into fostering seekers' engagement in OHCs based on cross-community interactions.

View free PDFSource page

Related papers

arxivcs.HC2026-07-24

The machine can say it but cannot hear it. Designed affective patterns and the expressive-sensing asymmetry in human-machine communication

Jan K. Argasinski

Affect-adaptive systems increasingly act as communicators that sense a user's emotion and respond with events meant to change it, closing an affective loop. This vision assumes both that a machine's affective messages are received and that the bodily channel it monitors carries a…

View free PDFSource page
arxivcs.HCcs.AI2026-07-24

Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education

Jennie Ren, Jordan H. McDowell, Kyrie Zhixuan Zhou

Generative AI is reshaping programming education, yet educators often infer students' AI-supported learning from classroom observations alone. This experience report presents a trio-ethnography involving two computing educators with different teaching philosophies and one undergr…

View free PDFSource page
arxivq-bio.QMcs.HC2026-07-24

Loom: Multi-Region Analysis of Spatial Transcriptomics with Local Neighborhoods and Global Trajectories

Siyuan Zhao, Nafiul Nipu, Hossein Fathollahian, Olga Karginova, Hao Chen, Ameen Salahudeen, et al.

We present Loom, a spatial transcriptomics (ST) visual computing system to support the analysis of pseudo-temporal trajectories, comparative investigation across samples and regions of interest, and the examination of spatially structured processes within local microenvironments.…

View free PDFSource page
arxivcs.HCcs.CL2026-07-24

Towards Reducing Foreign Language Anxiety Using Level-Appropriate Embodied Conversational Agents

Krishan Rajaratnam, Wenbin Gan, Yuan Sun

Foreign language anxiety (FLA) can be a major barrier to second language acquisition (SLA), especially in conversational contexts. With the proliferation of large language models (LLMs) throughout all areas of life, recent work suggests that interacting with LLM agents can be ins…

View free PDFSource page
arxivcs.HCcs.AIcs.LGcs.MM2026-07-24

Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability

Ahmed M. Abuzuraiq, Philippe Pasquier

Explainable AI (XAI) in creative practice can be less about technocentric explanation and more about enabling artists to inspect modify and debug models as part of making Yet largescale texttoimage diffusion systems are typically presented as opaque endtoend tools limiting this k…

View free PDFSource page