ITACOS at IberEval2017: Detecting stance in catalan and Spanish tweets

In this paper we describe the iTACOS submission for the Stance and Gender Detection in Tweets on Catalan Independence shared task. Concerning the detection of stance, we ranked as the first position in both languages outperforming the baselines; while in gender detection we ranked as fourth and third for Catalan and Spanish. Our approach is based on three diverse groups of features: stylistic, structural and context-based. We introduced two novel features that exploit signi ficant characteristics conveyed by the presence of Twitter marks and URLs. The results of our experiments are promising and will lead to future tailoring of these two features in a ffiner grained manner.