Topic Rehotting Prediction in Online Social Networks
Topic rehotting prediction is popular technique in social networks. It is really popular to detect hot topics, which can benefit many tasks including topic recommendations, the guidance of public opinions, and so on. However, in some cases, people may want to know when to re-hot a topic, i.e., make the topic popular again. In this paper, we address this issue by introducing a temporal User Topic Participation (UTP) model which models users behaviours of posting messages. The UTP model takes into account users interests, friend-circles, and unexpected events in online social networks. Also, it considers the continuous temporal modelling of topics, since topics are changing continuously over time. Furthermore, a weighting scheme is proposed to smooth the fluctuations in topic re-hotting prediction. Finally, experimental results conducted on real world data sets demonstrate the effectiveness of our proposed models and topic re-hotting prediction methods.