Abstract
In this paper we describe an initial attempt to build multi-faceted user models from raw Twitter data. The key contribution is to describe a technique for categorising users and their social ties according to a collection of curated topical categories and in this way resolve much of the preference noise that is inherent within user conversations. We go on to analyse and evaluate this approach on a data set of over 240,000 Twitter users and discuss the applications of these novel user models.
This work is supported by SFI under grant 07/CE/I1147 and by Amdocs Inc.
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Hannon, J., McCarthy, K., O’Mahony, M.P., Smyth, B. (2012). A Multi-faceted User Model for Twitter. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds) User Modeling, Adaptation, and Personalization. UMAP 2012. Lecture Notes in Computer Science, vol 7379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31454-4_26
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DOI: https://doi.org/10.1007/978-3-642-31454-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31453-7
Online ISBN: 978-3-642-31454-4
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