Event Analysis in Social Media Using Clustering of Heterogeneous Information Networks
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In this paper, we propose a novel approach for social media event finding in order to support fast access to information that users find relevant. While there are many approaches related to this problem, they mainly focus on homogeneous data, such as either the text of the posts, or the network of users. Our research focuses on combining multiple types of data from social media in a heterogeneous network. We propose different graph-based models using users, posts, and concepts extracted from the post content to represent the social media network. We analyse the resulted heterogeneous network, and use it in order to cluster posts by different topics and events. Our preliminary results show improvement over the methods that typically use only one type of data.