Name
Abstract | ||
|---|---|---|
Retweeting is an important action (behavior) on Twitter, indicating the behavior that users re-post microblogs of their friends. While much work has been conducted for mining textual content that users generate or analyzing the social network structure, few publications systematically study the underlying mechanism of the retweeting behaviors. In this paper, we perform an interesting analysis for the problem on Twitter. We have found that almost 25.5% of the tweets posted by users are actually retweeted from friends' blog spaces. Our investigation unveils that for the retweet behaviors, some statistics still follows the power law distribution, while some others violate the state-of-the-art distribution for Web. Based on these important observations, we propose a factor graph model to predict users' retweeting behaviors. Experimental results on the Twitter data set show that our method can achieve a precision of 28.81% and recall of 37.33% for prediction of the retweet behaviors. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1871437.1871691 | International Conference on Information and Knowledge Management, Proceedings, |
Keywords | Field | DocType |
social network,retweeting behavior,blog space,power law distribution,state-of-the-art distribution,important observation,retweet behavior,twitter data,important action,users re-post microblogs,factor graph,social influence | Factor graph,World Wide Web,Social network,Social media,Pareto distribution,Information retrieval,Computer science,Microblogging,Social influence,Recall | Conference |
Citations | PageRank | References |
118 | 4.09 | 8 |
Authors | ||
7 | ||