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 27\% higher F-measure than the best performed baseline method and for political data set, it was an increase by 29\%.
Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent 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 word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27\% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29\%.
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