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Taking into account the effects of both the network structures and the attributes of Likers and Likeds, we utilize a theory-grounded statistical modelling approach, Exponential Random Graph Models (ERGMs), to model the liking network in an online healthcare community. The results of ERGM analysis reveal that, while network degree exhibits a big effect in the liking process, individual attributes like the level of past involvement and degree of activity also positively influence members\u2019 future liking behaviour and performance. The evaluation indicates that our model is an effective method to identify the formation of liking networks. The findings extend the understanding of online liking behaviour and provide insights into harnessing the power of liking.<\/jats:p>","DOI":"10.1177\/0165551514558179","type":"journal-article","created":{"date-parts":[[2014,11,7]],"date-time":"2014-11-07T01:35:53Z","timestamp":1415324153000},"page":"89-96","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":16,"title":["Modelling liking networks in an online healthcare community: An exponential random graph model analysis approach"],"prefix":"10.1177","volume":"41","author":[{"given":"Xiaolong","family":"Song","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangbin","family":"Yan","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, People\u2019s Republic of 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