Online health communities (OHCs) have become a major source of support for people with health problems. This research tries to improve our understanding of social influence and to identify influential users in OHCs. The outcome can facilitate OHC management, improve community sustainability, and eventually benefit OHC users.
Through text mining and sentiment analysis of users’ online interactions, the research revealed sentiment dynamics in threaded discussions. A novel metric—the number of influential responding replies—was proposed to directly measure a user’s ability to affect the sentiment of others.
Using the dataset from a popular OHC, the research demonstrated that the proposed metric is highly effective in identifying influential users. In addition, combining the metric with other traditional measures further improves the identification of influential users.