What kind of content are you prone to tweet?: multi-topic preference model for tweeters

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• dc.contributor.author Recalde, Lorena
• dc.contributor.author Baeza Yates, Ricardo
• dc.date.accessioned 2021-04-22T09:34:53Z
• dc.date.available 2021-04-22T09:34:53Z
• dc.date.issued 2020
• dc.description Comunicació presentada a: BIAS 2020: Bias and Social Aspects in Search and Recommendation celebrat el 14 d'abril de 2020 a Lisboa, Portugal.
• dc.description.abstract According to tastes, a person could show preference for a given category of content to a greater or lesser extent. However, quantifying people’s amount of interest in a certain topic is a challenging task, especially considering the massive digital information they are exposed to. For example, in the context of Twitter, aligned with his/her preferences a user may tweet and retweet more about technology than sports and do not share any music-related content. The problem we address in this paper is the identification of users’ implicit topic preferences by analyzing the content categories they tend to post on Twitter. Our proposal is significant given that modeling their multi-topic profile may be useful to find patterns or association between preferences for categories, discover trending topics and cluster similar users to generate better group recommendations of content. In the present work, we propose a method based on the Mixed Gaussian Model to extract the multidimensional preference representation for 399 Ecuadorian tweeters concerning twenty-two different topics (or dimensions) which became known by manually categorizing 68.186 tweets. Our experiment findings indicate that the proposed approach is effective at detecting the topic interests of users.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Recalde L, Baeza-Yates R. What kind of content are you prone to tweet?: multi-topic preference model for tweeters. In: Boratto L, Faralli S, Marras M, Stilo G, editors. BIAS 2020: Bias and Social Aspects in Search and Recommendation; 2020 Apr 14; Lisbon, Portugal. Cham: Springer; 2020. p. 110-26. DOI: 10.1007/978-3-030-52485-2_11
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-030-52485-2_11
• dc.identifier.uri http://hdl.handle.net/10230/47191
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Boratto L, Faralli S, Marras M, Stilo G, editors. BIAS 2020: Bias and Social Aspects in Search and Recommendation; 2020 Apr 14; Lisbon, Portugal. Cham: Springer; 2020. p. 110-26
• dc.rights © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-52485-2_11
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Multidimensional profileen
• dc.subject.keyword User modelingen
• dc.subject.keyword Expectation maximizationen
• dc.subject.keyword Group recommender systemen
• dc.subject.keyword Topic modelingen
• dc.subject.keyword Twitteren
• dc.title What kind of content are you prone to tweet?: multi-topic preference model for tweetersen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion