Affiliation:
1. Harbin Institute of Technology, People’s Republic of China
Abstract
The value and role of the Like button in social media have gained increased attention/focus, yet we know little about how liking relations form between Likers and Likeds. We study this problem in an online healthcare context from a social network perspective. 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’ 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.
Subject
Library and Information Sciences,Information Systems
Cited by
12 articles.
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