Predicting the Popularity of Messages on Micro-blog Services

Micro-blogging is one of the most popular social media services on which users can publish new messages (usually called tweets), submit their comments and retweet their followees’ messages. It is retweeting behavior that leading the information diffusion in a faster way. However, why some tweets are more popular than others? Whether a message will be popular in the future? These problems have attracted great attention. In this paper, we focus on predicting the popularity of a tweet on Weibo, a famous micro-blogging service in China. It is important for tremendous tasks such as breaking news detection, personalized message recommendation, advertisement placement, viral marketing etc. We propose a novel approach to predict the retweet count of a tweet by finding top-

k

similar tweets published by the same author. To find the

top-k

similar tweets we consider both content similarity and temporal similarity. Meanwhile, we also integrate our method into a classical classification method and prove our method can improve the results significantly.