Tweet categorization by combining content and structural knowledge

• We explore the idea of integrating both textual and structural information.
• Using only structural information gives similar results to ones yielded BoW model.
• Complementing textual content with structural information achieves the best results.
• A proper combination scheme is critical when integrating both types of models.
• Experimental results show that our combination proposal is quite effective.

Twitter is a worldwide social media platform where millions of people frequently express ideas and opinions about any topic. This widespread success makes the analysis of tweets an interesting and possibly lucrative task, being those tweets rarely objective and becoming the targeting for large-scale analysis. In this paper, we explore the idea of integrating two fundamental aspects of a tweet, the proper textual content and its underlying structural information, when addressing the tweet categorization task. Thus, not only we analyze textual content of tweets but also analyze the structural information provided by the relationship between tweets and users, and we propose different methods for effectively combining both kinds of feature models extracted from the different knowledge sources. In order to test our approach, we address the specific task of determining the political opinion of Twitter users within their political context, observing that our most refined knowledge integration approach performs remarkably better (about 5 points above) than the textual-based classic model.

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