图书情报知识 ›› 2019, Vol. 0 ›› Issue (4): 53-61.doi: 10.13366/j.dik.2019.04.053

• 专题·面向国家安全的应急情报信息能力提升 • 上一篇    下一篇

恐怖事件情境下微博影响力的预测及演化

安璐, 易兴悦, 孙冉   

  • 出版日期:2019-07-10 发布日期:2019-09-10

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

  • Online:2019-07-10 Published:2019-09-10

摘要: [目的/意义]对恐怖事件情境下微博影响力进行预测并揭示其演化模式有助于反恐部门及时预见潜在的问题与危害,并迅速采取有效的应对措施。[研究设计/方法]本文提取了恐怖事件情境下微博的用户特征、时间特征和内容特征,构建了基于逻辑回归模型的恐怖事件微博影响力预测模型,并对预测模型进行训练和评估。[结论/发现] 模型的预测准确率达到85.8%,能有效地完成预测任务。此外,对恐怖事件中高影响力微博的特征进行分析和总结,最后,提出基于h指数的微博主题影响力量化方法,并分析了恐怖事件情境下微博主题影响力的演化规律。[创新/价值]研究结果有助于发现可能产生高影响力的恐怖事件相关微博,评估微博信息的传播规模,了解公众对恐怖事件的关注内容、强度及变化规律,协助反恐部门进行舆情管理。

关键词: 恐怖事件, 微博, 影响力, 预测, 演化, 主题识别, 情感分析, h指数

Abstract: [Purpose/Significance]The prediction of microblog entries' influence in the context of terrorist events and the revelation of their evolution patterns can help the counterterrorism departments foresee potential problems and hazards in time and take effective measures to respond quickly. [Design/Methodology]In this paper, the features of users, time and contents of microblog entries in terrorist incidents were extracted and a microblog influence prediction model was developed, trained and evaluated based on the logistic regression model. [Findings/Conclusion]The accuracy rate of the proposed model reaches 85.8%, which means it can effectively predict the influence of microblog entries. This paper analyzes the characteristics of highinfluence microblogs about terrorist incidents. A quantitative method of microblog topics’ influence according to h-index is proposed and the topics’ evolutionary patterns are further explored. [Originality/Value]The findings can help identify microblog entries of high influence about terrorist events, assess the scale of microblog information dissemination, understand the contents, intensity and evolution of public attention, and assist counter terrorism departments in public opinion management.

Key words: Terrorist events, Microblog, Influence, Prediction, Evolution, Topic identification, Sentiment analysis, Hindex