De-lurking in Online Communities Using Repost Behavior Prediction Method
Subject Areas : Semantic Web
Omid Reza
Bolouki Speily
1
(Urmia University of Technology)
Keywords: De-Lurking , Post Similarity , Lurker , Online Community,
Abstract :
Nowadays, with the advent of social networks, a big change has occurred in the structure of web-based services. Online community (OC) enable their users to access different type of Information, through the internet based structure anywhere any time. OC services are among the strategies used for production and repost of information by users interested in a specific area. In this respect, users become members in a particular domain at will and begin posting. Considering the networking structure, one of the major challenges these groups face is the lack of reposting behavior. Most users of these systems take up a lurking position toward the posts in the forum. De-lurking is a type of social media behavior where a user breaks an "online silence" or habit of passive thread viewing to engage in a virtual conversation. One of the proposed ways to improve De-Lurking is the selection and display of influential posts for each individual. Influential posts are so selected as to be more likely reposted by users based on each user's interests, knowledge and characteristics. The present article intends to introduce a new method for selecting k influential posts to ensure increased repost of information. In terms of participation in OCs, users are divided into two groups of posters and lurkers. Some solutions are proposed to encourage lurking users to participate in reposting the contents. Based on actual data from Twitter and actual blogs with respect to reposts, the assessments indicate the effectiveness of the proposed method.
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