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dc.contributor.authorWood, Ian D.
dc.identifier.citationWood, Ian D. (2015, October 19 - 23, 2015). Proceedings of the 2015 Workshop on Topic Models: Post-Processing and Applications. Paper presented at the CIKM'15 24th ACM International Conference on Information and Knowledge Management, Melbourne, VIC, Australia.en_IE
dc.description.abstractWhen studying large social media data sets, it is useful to reduce the dimensionality of both the network (e.g. by finding communities) and user-generated data such as text (e.g. using topic models). Algorithms exist for both these tasks, however their combination has received little attention and proposed models to date are not scalable (e.g.: [4]). One approach to such combined modelling is to perform community and topic modelling independently and later combine the results. In the case of overlapping communities, this combination requires a method for attributing each users topic usage to the communities in which she participates. This paper presents a Bayesian model for attributing individual documents to communities which balances the users proportional community membership with community topic coherence. Community topic usage is modelled with a Dirichlet distribution with fixed concentration parameter, leading to a well defined conjugate prior. Thought the prior is computationally expensive, the already reduced dimensionality in both topics and communities make a tractable algorithm feasible, even for large data sets. The model is applied to a corpus of tweets and twitter follower relations collected on hash tags used by people with eating disorders [14].en_IE
dc.description.sponsorshipScience Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (INSIGHT), European Union supported projects LIDER (ICT-2013.4.1-610782), MixedEmotions (H2020-644632).en_IE
dc.relation.ispartof2015 Workshop on Topic Models: Post-Processing and Applications (CIKM)en
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.subjectTopic modelsen_IE
dc.subjectCommunity detectionen_IE
dc.subjectBayesian inferenceen_IE
dc.subjectConjugate prioren_IE
dc.subjectDirichlet distributionen_IE
dc.subjectAuthor community membershipen_IE
dc.titleCommunity topic usage in social networksen_IE
dc.typeConference Paperen_IE
dc.local.contactIan Wood. Email:

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland