Bursty topic detection method for microblog based on time series analysis - Min HE; Jie2 XU; Pan1 DU; Xue-qi1 CHENG; Li-hong WANG (Tongxin xuebao)
Detecting bursty topics from microblogs was an important task to understand the current events attracting a large number of internet users.However,the existing hods suitable for news articles cannot be adopted directly for microblogs.Because microblogs have unique characteristics compared wi formal texts,including diversity,dynamic and noise.A detection method for microblog bursty topic was proposed based on time series analysis,which was an op-timization method of momentum model.The candidate bursty features were extracted by momentum model.The time se-ries of feature's momentum were modled by frequency domain analysis theory and stock trend analysis theory.The fre-quently pseudo-bursty features were filtered according to analysis results of frequency-domain characteristics.The inter-mittently pseudo-bursty features were filtered according to the novelty analysis result through stock trend theory.The bursty topics were finally emerged with combination of effective bursty features.The experiments are conducted on a real Sina microblog data set.It show that the proposed method improves the precis and F-measure remarkably compared with the momentum modle.