Abstract
We present a methodology for identifying user communities on Twitter, by defining a number of similarity metrics based on their shared content, following relationships and interactions. We then introduce a novel method based on latent Dirichlet allocation to extract user clusters discussing interesting local topics and propose a methodology to eliminate trivial topics. In order to evaluate the methodology, we experiment with a real-world dataset created using the Twitter Searching API.
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© 2015 Springer International Publishing Switzerland
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Vathi, E., Siolas, G., Stafylopatis, A. (2015). Mining Interesting Topics in Twitter Communities. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9329. Springer, Cham. https://doi.org/10.1007/978-3-319-24069-5_12
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DOI: https://doi.org/10.1007/978-3-319-24069-5_12
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