Abstract
The rise in the popularity of Social Networking Sites has made Community Detection in such networks a major research interest. The edges connecting the entities in the network are the principal foci in graphical community detection. At the same time, large volume of data is produced on these Social Networking Sites, a large portion of which being text data. Document Clustering methods utilize the textual properties of text documents to cluster similar documents together while separating dissimilar documents. This paper treats text data collected from Twitter as a set of documents. The clusters produced by the document clustering methods are associated with the respective users. These clusters are then compared with the communities detected in the graphical representation of the network generated from the users and the relationships between them. NodeXL was used to collect data from Twitter while Gephi was used for visualizing the collected dataset. Different feature representation and clustering methods were applied for clustering the tweets(documents) and in turn the users associated with them.
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Bhowmik, K., Ralescu, A. (2021). Taking a Close Look at Twitter Communities and Clusters. In: Simian, D., Stoica, L.F. (eds) Modelling and Development of Intelligent Systems. MDIS 2020. Communications in Computer and Information Science, vol 1341. Springer, Cham. https://doi.org/10.1007/978-3-030-68527-0_12
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