Corpus-Based Augmented Media Posts with Density-Based Clustering for Community Detection

This paper proposes a corpus-based media posts expansion technique with a density-based clustering method for community detection. To enrich the user content information, firstly all (short-text) media posts of a user are combined with hash tags and URLs available with the posts. The expanded content view is further augmented by the virtual words inferred using the novel concept of matrix factorization based topic proportion vector approximation. This expansion technique deals with the extreme sparseness of short text data which otherwise leads to insufficient word co-occurrence and, in hence, inaccurate outcome. We then propose to group these augmented posts which represent users by identifying the density patches and form user communities. The remaining isolated users are then assigned to communities to which they are found most similar using a distance measure. Experimental results using several Twitter datasets show that the proposed approach is able to deal with common issues attached with (short-text) media posts to form meaningful communities and attain high accuracy compared to relevant benchmarking methods.