Abstract
Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with those who face similar problems and are involved in different types of social supports, such as informational support, emotional support and companionship. Using a case study of an OHC among breast cancer survivors, we first use machine learning techniques to reveal the types of social support embedded in each post from an OHC. Then we generate each user’s contribution profile by aggregating the user’s involvement in various types of social support and reveal that users play different roles in the OHC. By comparing online activities for users with different roles and conducting survival analysis on users’ time span of online activities, we illustrate that users’ levels of engagement in an OHC are related to various types of social support in different ways.
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