Topic Propagation Prediction Based on Dynamic Probability Model

Abstract

As social networks play an increasingly important role in people's lives, people are more likely to discuss hot topics on social networks. Predicting the spread of hot topics, known as topic propagation prediction is an important task. Due to the unpredictability of the users and topics in social networks, predicting the topic propagation trend is still a major challenge. Different users play different roles in topic propagation. However, existing studies have not utilized user role analysis. In this paper, we propose a topic propagation prediction method (TPP) based on user role analysis and dynamic probability model. First, we describe our user role analysis, which incorporates four user-factors to characterize user attributes along two dimensions. Second, we combine dynamic probability model with user role analysis to accurately predict the topic propagation trend. Finally, we prove the efficiency of TPP by experiments.