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According to tastes, a person could show preference for a given category ofcontent to a greater or lesser extent. However, quantifying people's amount ofinterest in a certain topic is a challenging task, especially considering themassive digital information they are exposed to. For example, in the context ofTwitter, aligned with his/her preferences a user may tweet and retweet moreabout technology than sports and do not share any music-related content. Theproblem we address in this paper is the identification of users' implicit topicpreferences by analyzing the content categories they tend to post on Twitter.Our proposal is significant given that modeling their multi-topic profile maybe useful to find patterns or association between preferences for categories,discover trending topics and cluster similar users to generate better grouprecommendations of content. In the present work, we propose a method based onthe Mixed Gaussian Model to extract the multidimensional preferencerepresentation for 399 Ecuadorian tweeters concerning twenty-two differenttopics (or dimensions) which became known by manually categorizing 68.186tweets. Our experiment findings indicate that the proposed approach iseffective at detecting the topic interests of users.

What kind of content are you prone to tweet? Multi-topic Preference Model for Tweeters