A real-time monitoring method of public opinion evolution based on dynamic social network

Due to the popularity of social media, rumors spread rapidly on social network, which has been damaging the public trust system and social stability. In recent years, the research on rumor monitoring and early warning has attracted much researchers’ attention. In order to make full use of social network interaction information, researchers use this information to enhance tweets text to improve the performance of rumor monitor. However, most of these methods have to obtain static and complete network structures before their algorithms work, while nodes and edges are constantly evolving in practice. These methods are not only ineffective in evolutionary network structures, but also time and memory consuming, which will directly affect the feasibility of dynamic monitoring of Internet public opinion. In view of the above challenge, this paper proposes a dynamic graph learning method based on interactive information of social networks, which integrates network structure, context semantic and sequential information, especially simplifies the computational complexity of social network evolution. We have carried out a large number of experiments on a largescale real dataset. The experimental results show that our proposed model is better than the existing rumor detection methods.