Microblog Burst Keywords Detection Based on Social Trust and Dynamics Model  

作　　者：DONG Guozhong LI Ruiguang YANG Wu WANG Wei GONG Liangyi SHEN Guowei YU Miao LV Jiguang 

机构地区：[1]Information Security Research Center, Harbin Engineering University [2]National Computer Network Emergency Response Technical Team/Coordination Center

出　　处：《Chinese Journal of Electronics》2014年第4期695-700,共6页电子学报（英文版）

基　　金：supported by the National High Technology Research and Development Program of China(No.2012AA012802);the National Natural Science Foundation of China(No.61170242,No.61101140,No.61272537);the Fundamental Research Funds for the Central Universities(No.HEUCF100611)

摘　　要：Microblog has emerged as a popular medium for providing new sources of information and rapid communications, particularly during burst topics. Burst keywords detection from real-time microblog streams is important for burst topics detection. The exiting algorithms may detect fake burst keywords without taking into account the trustworthiness of the users and human's daily timetable. Our work is the first to combine the trustworthiness of the users with burst keywords detection. We propose a novel approach to detect burst keywords based on social trust and dynamics model. We adapt basic notions of dynamics from physics and model keywords bursts as momentum change of the keywords. On the analogy of physical dynamics model, this approach defines mass as the trustworthiness of user and position as the frequency of keywords. We compute each keyword's burst value by using Moving average convergence/divergence(MACD) and determine whether it is a burst keyword in a given time window. The experimental results on large-scale Sina microblog dataset show that the proposed approach can avoid detecting fake burst keywords.Microblog has emerged as a popular medium for providing new sources of information and rapid communications, particularly during burst topics. Burst keywords detection from real-time microblog streams is important for burst topics detection. The exiting algorithms may detect fake burst keywords without taking into account the trustworthiness of the users and human＇s daily timetable. Our work is the first to combine the trustworthiness of the users with burst keywords detection. We propose a novel approach to detect burst keywords based on social trust and dynamics model. We adapt basic notions of dynamics from physics and model keywords bursts as momentum change of the keywords. On the analogy of physical dynamics model, this approach defines mass as the trustworthiness of user and position as the frequency of keywords. We compute each keyword＇s burst value by using Moving average convergence/divergence （MACD） and determine whether it is a burst keyword in a given time window. The experimental results on large-scale Sina microblog dataset show that the proposed approach can avoid detecting fake burst keywords.

关 键 词：Sina microblog Burst keywords detection Social trust Dynamics model. 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]