Finding influential users of an online health community: a new metric   based on sentiment influence
  What characterizes influential users in online health communities (OHCs)? We hypothesize that (1) the emotional support received by OHC members can be assessed from their sentiment ex-pressed in online interactions, and (2) such assessments can help to identify influential OHC members. Through text mining and sentiment analysis of users' online interactions, we propose a novel metric that directly measures a user's ability to affect the sentiment of others. Using dataset from an OHC, we demonstrate that this metric is highly effective in identifying influential users. In addition, combining the metric with other traditional measures further improves the identification of influential users. This study can facilitate online community management and advance our understanding of social influence in OHCs. 
Kang Zhao, Greta Greer, Baojun Qiu, Prasenjit Mitra, Kenneth Portier,   and John Yen
cs.SI cs.CY physics.soc-ph