[Objective] This paper constructs a topic drift index for trending network events, aiming to describe their changing topics. [Methods] We used the LDA model to extract topics of online trending events and analyzed their drifts with word weights. Then, we proposed procedures for constructing topic drift index. Finally, we took “Gao Yixiang pass away” as the sample for an empirical analysis. [Results] In the early stage of our case, the number of topics increased from 11 to 18, and the topic drift index was 41%, which then fell to 22%. Finally, the number of topics was reduced to 5 and the topic drift index turned to -41%. [Limitations] The proposed method could not effectively generate early-warnings for small number of topic changes and multimedia contents. It cannot detect changes of topic semantics. [Conclusions] The topic drift index for trending network events could predict the timing of online public opinion outbreaks and their recurrences.
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Huang Wei,Zhao Jiangyuan,Yan Lu. Empirical Research on Topic Drift Index for Trending Network Events. Data Analysis and Knowledge Discovery, 2020, 4(11): 92-101.
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