Abstract

In recent years, the number of online health communities (OHCs) has increased rapidly as more patients seek to access alternate sources of health information and connect with other patients who have similar health concerns. However, insufficient attention has been paid to investigating user identities in OHCs. To address this potential research gap, by elaborating on the communication theory of identity, this study presents a multi-layered framework to analyze the different layers of user identities that are portrayed in OHCs. Through coding analysis, we discovered that the personal-layer identities that appear in OHCs are patients, partners, offspring, parents, friends and relatives, and others. Moreover, a series of detection models for the personal-layer identities of users were developed, which incorporated content features into machine learning approaches, and they achieved F1-scores above 0.88. Furthermore, we analyzed the features of enactment-layer identities presented by users’ posting behavior and content and the impact of the personal-layer identities of users on the features of the enactment-layer identities. The findings suggested that the features of the enactment-layer identities differed significantly among users with diverse personal-layer identities in terms of both behaviors and communication needs. Users who were identified as patients served as both information seekers and providers, whereas users with the personal-layer identities of parents tended to engage in the community continuously. Our findings extend the understanding of user identities within the context of OHCs.

Full Text
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