SIG-HLTH Best Health-Related ASIS&T 2025 Annual Meeting Long Paper Award

Title: When Chatman Meets Chinese Rural Older People with Health Anxiety: From Life in the Round to Concentric Life Circles

Authors: Lin Wang, Xinyi Wu, and Huajian Zhu, Chinese Academy of Science and Education Evaluation, Hangzhou Dianzi University, China

Abstract: Rural Chinese older people face significant information poverty and elevated health anxiety, yet their health information behavior remains understudied in non-Western contexts. This study employs in-depth interviews, grounded theory, and information horizon mapping to investigate the health information seeking patterns of this vulnerable group. Our findings reveal a concentric life circle pattern, where health information behavior radiates outward from the core life circle (kinship ties/close community) to transitional life circles (extended networks). By integrating Chatman's small world theory and life in the round theory with China's localized context, we propose the original "Concentric Life Circle Theory" (CLCT). This theory advances cross-cultural information behavior research and offers actionable solutions to mitigate health anxiety through culturally tailored information interventions.

SIG-HLTH Best Health-Related ASIS&T 2025 Annual Meeting Short Paper Award

Title: Construction and Representation Learning of Social Heterogeneous Information Networks based on Multimodal Fusion and Enhanced-HGCN

Authors: Wei Zhou, Lu An, Ruilian Han, and Gang Li, Center for Studies of Information Resources, School of Information Management, Wuhan University

Abstract: During public health events, social media platforms serve as key channels for disseminating multimodal information especially from government departments. These data are crucial for enhancing public understanding and emergency preparedness. This study proposes a novel framework for constructing and learning representations from social heterogeneous information networks (SHINs) based on multimodal fusion and an Enhanced-HGCN (Hyperbolic Graph Convolutional Network) model. Approximately 74,403 flu-related microblog posts were collected, from which multimodal features were extracted to construct a heterogeneous network linking users, posts, and topics. Furthermore, the Enhanced-HGCN model with a two-layer graph convolution structure is proposed to learn node embeddings in the SHINs. Experimental results show that our approach significantly outperforms other baseline models including in clustering performance. This research validates the feasibility of multimodal SHINs construction and the effectiveness of the Enhanced-HGCN, providing a foundation for future applications such as knowledge recommendation and cross-platform information collaboration.

SIG-HLTH Best Health-Related ASIS&T 2025 Annual Meeting Poster Award

Title: Use of Eye-Tracking as a Method for Health Information Behavior Research 

Authors: Sue Yeon Syn and Laura Lannan, Department of Information Sciences, The Catholic University of America  

Abstract: This paper analyzes 25 health information behavior studies published in the period of 15 years (2009-2023) using eye-tracking as the research methodology. Eye-tracking technology has become a valuable tool to study information behavior. This study examines the ways of adopting eye trackers in health information behavior research and how the research is designed with an extended method the technology provides. This paper contributes to understanding methodological trends for health information behavior research.