An event extraction model based on timeline and user analysis in Latent Dirichlet Allocation

Social media such as Twitter has come to reflect the reaction of the general public to major events. Since posts are short and noisy, it is hard to extract reliable events based on word frequency. Even though an event term appears in a particularly low frequency, as long as at least one reliable user mentions the term, it should be extracted. This paper proposes an event extraction method which combines user reliability and timeline analysis. The Latent Dirichlet Allocation (LDA) topic model is adapted with the weights of event terms on timeline and reliable users to extract social events. The reliable users are detected on Twitter according to their tweeting behaviors: socially well-known users and active users. Reliable and low-frequency events can be detected based on reliable users In order to see the effectiveness of the proposed method, experiments are conducted on a Korean tweet collection; the proposed model achieved 72% in precision. This shows that the LDA with timeline and reliable users is effective for extracting events on the Twitter test collection.