Social computing and weighting to identify member roles in online communities

As more and more people join online communities, the ability to better understand members' roles becomes critical to preserving and improving the health of those communities. We propose a novel approach to identifying key members and their roles by discovering implicit knowledge from online communities. Viewing an online community as a social network connected by poster-poster relationships, the approach takes advantage of the strengths of social network analysis and weighting schemes from information retrieval in identifying key members. Experimental studies were carried out to empirically evaluate the proposed approach with real-world data collected from a Usenet bulletin board over a one year period. The results showed that the proposed approach can not only identify prominent members whose behaviors are community supportive but also filter chatters whose behaviors are superficial to the online community. The findings have broad implications for online communities by allowing moderators to better support their members and by enabling members to better understand the conversation space.