Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media

Event detection in social media refers to automatic identification of important information shared in social media platforms on a certain time. Considering the dynamic nature and high volume of data production in data streams, it is impractical to filter the events manually. Therefore, it is important to have an automated mechanism to detect events in order to utilise social media data effectively. Analysing the available literature, most of the existing event detection methods are only focused on statistical and syntactical features in data, even though the underlying semantics are also important for an effective information retrieval from text, because they describe the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in prediction-based word embeddings and hierarchical agglomerative clustering. The adoption of prediction-based word embeddings incorporates the semantical features in the text to overcome a major limitation available with previous approaches. This method is experimented on two recent social media data sets which represent the sports and politics domains. The results obtained from the experiments reveal that our approach is capable of effective and efficient event detection with the proof of significant improvements over baselines. For sports data set, Embed2Detect achieved 30% higher F-measure than the best performed baseline method and for political data set, it was an increase by 36%.