報告人：李昕 教授 香港城市大學
時間：2021年11月23日（周二）下午15:00-16:30
騰訊會議號：【875 521 189】
報告內容簡介：
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.
報告人簡介:
李昕，香港城市大學信息系統(tǒng)系教授。他于亞利桑那大學獲得管理信息系統(tǒng)博士學位，于在清華大學自動化系獲得學士和碩士學位。研究興趣包括數(shù)字經濟、醫(yī)療保健、數(shù)據(jù)科學/機器學習、社會網(wǎng)絡和應用計量經濟學。李昕教授的成果發(fā)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數(shù)超過3000,H指數(shù)為29。他是IEEE和ACM的高級成員，INFORMS和AIS的成員。
（承辦：管理工程系、科研與學術交流中心）
