Argument Quality Assessment in Brazilian political tweets

Argumentation is an inherent skill in human com-munication, both in oral and written situations. Well-founded arguments are important to support decision-making and learning, as well as to reach widely accep-ted conclusions. As a research area, argumentation is a multidisciplinary field that studies the processes of debate and reasoning. In computational linguistics, investigations have been carried out to (i) identify ar-guments and their units and (ii) generate or (iii) evalu-ate the quality of arguments. However, most current work focuses on argument mining in formal English texts. In this article, we evaluated the quality of argu-mentation in political domain tweets, written in Bra-zilian Portuguese, using traditional machine learning algorithms - such as Logistic Regression, KNearest Neighbor, Decision Trees, Support Vector Machines (SVM), Random Forest and Naive Bayes - and also a fine-tuning of two neural models (BERTimbau and RobertaTwitterBR). In addition to bringing practical results for the assessment of argumentation quality in a challenging textual genre, such as Twitter, and in a controversial domain, such as Brazilian politics, this article also aims to fill in the lack of works that au-tomatically assess the quality of arguments in Portu-guese. Among the evaluated classification algorithms, the model obtained from the fine-tuning of BERTim-bau presented the best results, with an accuracy of 69.65% when all classes were considered and 100.00% for messages with high quality of argumentation.