Classificação automática de discursos de ódio em textos do twitter

Abstract

Hate Speech can be defined as any communication that denigrates a person by their ethnicity, race, religion, sexual orientation, nationality or other characteristics. This behavior is becoming increasingly common in social networks, where many people confuse freedom of expression with intolerance. Young people are the main users affected since they represent a portion which might be easier to be influenced by the ideology propagated by haters, which in turn spread violence, racism, xenophobia, religious intolerance, etc. The tool that might help to handle this issue is Text Mining, which is capable of capture patterns or trends of texts in natural language. This task can be defined as a method of extracting relevant information in unstructured databases or semi-structured. Given that the Twitter is one of the most used social networks in Brazil, this work aims to implement and evaluate supervised machine learning techniques in order to automatically identify hate speech in tweets. With that in mind, we build a corpus with data collected from Twitter and part of it is manually annotated and subsequently preprocessed so we can obtain the ground truth collection used for training and evaluation of the supervised models. Finally, we conduct a comparison between machine learning algorithms, namely the SVM, Naive-Bayes and Logistic Regression. Later, we identify the best model under the described domain.