Prediction and Evolution of the Influence of Microblog Entries in the Context of Terrorist Events

Abstract

The outbreak of terrorist events often causes tremendous damage to the country and society and arouses high attention from the public and an overwhelming response on the microblogging platform. Predicting the influence of microblogging in the context of terrorist events and revealing its evolutionary mode can help counterterrorism departments foresee potential risks, take effective countermeasures in time, and provide a reference for reducing public panic caused by terrorist events. In this study, Word2Vec is combined with the K-means clustering technique to discover the topics of microblogging, and an emotion analysis of microblogging is performed. The user features, time features, and content features of microblogging in the context of terrorist events are extracted. The prediction model of microblogging influence based on the logistic regression model was constructed and evaluated. The experimental results showed that the prediction accuracy of the model was 85.8%, which had superior performance over other six classification models. In addition, the high-influence characteristics of microblogging in the context of terrorist events were analyzed and summarized. Finally, a quantitative method of the influence of a microblogging topic based on the h-index was proposed. The evolution pattern of the influence of a microblogging topic was analyzed. The results can help predict microblog entries of high influence, understand the intensity and variation of public concern over terrorist events, and assist counterterrorism departments in taking scientific decisions.