Event Detection and Identification of Influential Spreaders in Social Media Data Streams

作     者：Leilei Shi Yan Wu Lu Liu Xiang Sun Liang Jiang 

作者机构：School of Computer Science and Telecommunication Engineering Jiangsu University Department of Computing and Mathematics University of Derby 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2018年第1卷第1期

页      面：34-46页

核心收录：

学科分类：08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 

基　　金：supported by the National Natural Science Foundation of China(Nos.61502209 and 61502207) the Natural Science Foundation of Jiangsu Province of China(No.BK20130528) Visiting Research Fellow Program of Tongji University(No.8105142504) 

主　　题：event detection microblogging Hypertext-Induced Topic Search(HITS) Latent Dirichlet Allocation(LDA) identification of influential spreader 

摘      要：Microblogging, a popular social media service platform, has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for detecting newly emerging events and for identifying influential spreaders who have the potential to actively disseminate knowledge about events through microblogs. However, traditional event detection models require human intervention to detect the number of topics to be explored, which significantly reduces the efficiency and accuracy of event detection. In addition, most existing methods focus only on event detection and are unable to identify either influential spreaders or key event-related posts, thus making it challenging to track momentous events in a timely manner. To address these problems, we propose a Hypertext-Induced Topic Search(HITS) based Topic-Decision method(TD-HITS), and a Latent Dirichlet Allocation(LDA) based Three-Step model(TS-LDA). TDHITS can automatically detect the number of topics as well as identify associated key posts in a large number of posts. TS-LDA can identify influential spreaders of hot event topics based on both post and user *** experimental results, using a Twitter dataset, demonstrate the effectiveness of our proposed methods for both detecting events and identifying influential spreaders.