Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Predicting popular messages in Twitter
Proceedings of the 20th international conference companion on World wide web
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
A Twitter-based smoking cessation recruitment system
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
RAProp: ranking tweets by exploiting the tweet/user/web ecosystem and inter-tweet agreement
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Identifying interesting Twitter contents using topical analysis
Expert Systems with Applications: An International Journal
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Millions of posts are being generated in real-time by users in social networking services, such as Twitter. However, a considerable number of those posts are mundane posts that are of interest to the authors and possibly their friends only. This paper investigates the problem of automatically discovering valuable posts that may be of potential interest to a wider audience. Specifically, we model the structure of Twitter as a graph consisting of users and posts as nodes and retweet relations between the nodes as edges. We propose a variant of the HITS algorithm for producing a static ranking of posts. Experimental results on real world data demonstrate that our method can achieve better performance than several baseline methods.