  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%.
