Microblog Bursty Events Detection Method Based on Multiple Word Features

In recent years, a wide variety of bursty events have been occurring frequently in many fields, impacting both the stability and the development of our society. This paper proposes an event detection model based on multiple word features, which is intended to detect bursty events in the massive microblog data. The model will assist decision-makers to monitor microblogs and guide public opinions and will minimize the negative effect of bursty events to society. Firstly, the model slices the microblog data according to the time information. In each time window, the word frequency feature, the topic tag feature and the word frequency growth rate feature of each word are calculated separately. Then, the D-S evidence theory and the analytic hierarchy process are utilized to determine each word's feature weights, which are then merged to obtain the bursty feature value of the word. Words with large bursty feature value are selected to form the bursty feature word set and to construct a coupling degree matrix of bursty feature word set based on co-occurrence degree and tightness. Finally, the coupling degree matrix is used as the input of the hierarchical agglomerative clustering algorithm to generate a binary tree with bursty words being leaf nodes, and the internal similarity binary tree pruning algorithm is used to divide the clustering results. In this way, the detection of the corresponding time window's bursty events can be realized. The experimental results show that the event detection model based on bursty words has the best effect when the intra-cluster similarity threshold is 1.1, the correct rate is as high as 0.8462, the recall rate reaches 0.8684, and the F value is 0.8571, indicating the effectiveness of the proposed method. © 2019, Chinese Institute of Electronics. All right reserved.