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Hot Event Detection for Social Media Based on Keyword Semantic Information

Statistical features are commonly used to detect hot events in social media. However, these features cannot represent the semantic similarity of synonym expression. Aiming at this problem, a semantic keyword-based hot event detection method for social media is proposed in this paper, which includes a semantic keywords model to distinguish real-world events from hot topics. The clusters are found using Affinity Propagation with statistic features. Also, related discussions from different perspectives of the event are merged using word embedding. After that, news titles and posts of hot topics are employed to train the semantic keywords model. This model is used to distinguish real-world events from clusters. Experiments show the proposed method can find hot events from social media data and related posts for further research.