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