Kang Zhao1, John Yen2, Greta Greer3, Baojun Qiu4, Prasenjit Mitra2, Kenneth Portier3. 1. Department of Management Sciences, The University of Iowa, Iowa City, Iowa, USA. 2. College of Information Sciences and Technology, The Pennsylvania State University, University Park, Pennsylvania, USA. 3. American Cancer Society, Atlanta, Georgia, USA. 4. eBay Inc, San Jose, California, USA.
Abstract
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Entities:
Keywords:
cancer survivors; influential users; online health community; sentiment analysis and influence
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