LiveTweet: Monitoring and Predicting Interesting Microblog Posts

摘要

This paper describes the LiveTweet application, a system for automatically analysing and predicting the interestingness of microblog posts. Based on a stream of recent microblog posts the system tracks user interactions on Twitter that indicate interesting content. An incremental Naive Bayes model is trained to learn the characteristics of tweets which are considered interesting by the users. Finally, the probability of a microblog post to be retweeted is used as metric for its interestingness.