Social networking sites (SNSs) facilitate self-expression and promote social connections. There has been growing scholarly attention to the affect-charged collectivities created online in the aftermath of disasters and mass traumas. This study was designed to examine how individuals affiliate in SNS-based commemoration of a mass trauma, taking advantage of a large Weibo (the Chinese equivalent of Twitter) data set which captures users’ responses over 4 years to the anniversary of the Nanjing massacre, a major traumatic event in Chinese history. Machine learning–based content analysis was combined with dyadic-level network analysis to examine the content Weibo users create and the conversational structures they formed. The results reveal that homophily, geographic proximity, and preferential attachment work in tandem with displays of emotion to influence the formation of online conversational ties. Expressions of negative emotions were found to facilitate or inhibit the homophily effect. Being exposed to the display of anger amplifies the homophily effect among the users, while sadness weakens it. The findings point to the importance of examining specific emotions rather than global (positive–negative) feelings in understanding the dynamics of SNS-based interaction.
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