报告人：李昕 教授 香港城市大学
腾讯会议号：【875 521 189】
Online health communities (OHCs) play an important role in enabling patients to exchange information and obtain social support from each other. However, do OHC interactions always benefit patients? In this research, we investigate different mechanisms by which the sentiment of OHC content may affect patients’ moods. Specifically, we notice users can read not only emotional support intended to help them, but also emotional support targeting other persons or posts unintended to generate any emotional support (named auxiliary content). Drawing from emotional contagion theories, we argue even though emotional support may benefit targeted support seekers, it could have a negative impact on the moods of other patients. Our empirical study on an OHC for depression patients supports these arguments. Our findings are new to the literature and critical to practice since they suggest that we should carefully manage OHC-based interventions for depression patients. In the follow-up analysis, we show the possibilities to alter the intervention volume, length, and frequency to tackle the challenge of the negative effect. In the study, we design a novel deep learning model to differentiate emotional support from auxiliary content. We show this differentiation is critical for identifying the negative effect of emotional support on unintended recipients.
李昕，香港城市大学信息系统系教授。他于亚利桑那大学获得管理信息系统博士学位，于在清华大学自动化系获得学士和硕士学位。研究兴趣包括数字经济、医疗保健、数据科学/机器学习、社会网络和应用计量经济学。李昕教授的成果发表在MISQ, ISR, JMIS, INFORMS JOC, DSS, I&M, JASIST, IEEE/ACM Transactions，Nature Nanotechnology等期刊上。他的成果谷歌引用次数超过3000,H指数为29。他是IEEE和ACM的高级成员，INFORMS和AIS的成员。