Topic dynamics in Weibo: Happy Entertainment dominates but angry Finance is more periodic

Abstract

The tremendous development of online social media have changed people's life fundamentally in recent years. Weibo, a Twitter-like service in China, has attracted more than 500 million users in less than four years and produces more than 100 million Chinese tweets every day. In these massive tweets, different user interests and daily trends are reflected by different topics. While to our best knowledge, a systematic investigation of topic dynamics in Weibo is still missing. Aiming at filling this vital gap, we try to disclose the evolving patterns of topics from the perspective of time, geography, gender, emotion and interaction. First, an incremental learning framework is established to classify more than 200 million tweets into seven topics fast and accurately, whose F-measure arrives as high as 84%. Second, many interesting patterns in topic dynamics are revealed. For instance, happy Entertainment accounts for over half of the tweets and angry Finance possesses the most significant periodic pattern. Besides, the female and male users prefer different topics and Finance shows a surprisingly high correlation between connected users. Finally, our findings could provide insights for the topic-related applications in social media, like event detection or content recommendation.