基于网络结构与内容分布的新媒体事件聚类研究

Purpose/Significance］The existing clustering research of new media events focuses on the single-dimensional attributes of events, and does not consider the network structure characteristics and text distribution characteristics of event propagation. ［Method/Process］This research was inspired by the concept of information entropy, and proposed an event clustering model based on network structure entropy and content distribution entropy. The model completed cross-content event similarity comparison on the basis of characterizing event network structure characteristics and content distribution characteristics, then Network Representation Learning algorithm and k-means clustering algorithm cluster the events. This paper selected 113 microblog events as the experimental objects, and used the basic attributes of the events(likes, comments, reposts, etc.)as the cluster control experimental group. ［Results/Conclusion］The analysis of the experimental results showed that the model proposed in this study could capture the deeper communication and distribution characteristics of new media events. At the same time, it could improve and supplement existing similarity calculation indicators. ［Originality/Value］This research can not only extract the propagation characteristics of the event from multidimensional levels, that is, the characteristics of the event network structure and the distribution of event content. Also it can provide support for public opinion prediction and control. The model can also monitor the communication commonality between different events through the entropy dimension of information changes to assist subsequent reflection on public opinion events.