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

TL;DR

This paper introduces a method using a Mixed Gaussian Model to identify Twitter users' multi-topic preferences based on their tweets, aiding in understanding interests and improving content recommendations.

Contribution

It presents a novel approach to quantify multi-topic preferences of Twitter users through a Gaussian mixture model, based on a large dataset of categorized tweets.

Findings

Effective detection of user interests in multiple topics.

Successful modeling of multi-topic preference profiles.

Potential applications in content recommendation and user clustering.

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.