The Impact of Reviews of Physicians on Patient Choice

How reviews impact patient choice in health field is still unknown. Patients often worry about their diseases and are eager to find a high skill physician to cure their painful. Traditional hospitals often lack information about individual physician, and with the emergence of online health communities (OHCs), patients can get physician service information on the platform. This study researches the role of reviews in health field and how the roles change with different diseases by collecting data from an online health community. We divide patient reviews into two kinds: online service reviews and offline service reviews based on different services. We find disease risk significantly moderates the relationship between reviews and patient choice: when patients get high-risk diseases, they care more offline service reviews than low-risk diseases. On the contrary, when patients get low-risk diseases, they care more online service reviews than high-risk diseases

Shaping the Emerging Norms of Using Large Language Models in Social Computing Research

The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analyzing vast amounts of textual data and generating human-like responses, enabling researchers to delve into complex social phenomena. On the other hand, concerns are emerging regarding the validity, privacy, and ethics of the research when LLMs are involved. This SIG aims at offering an open space for social computing researchers who are interested in understanding the impacts of LLMs to discuss their current practices, perspectives, challenges when engaging with LLMs in their everyday work and collectively shaping the emerging norms of using LLMs in social computing research