Abstract
Consistently with social and political concern about hatred and harassment through social media, in recent years, automatic hate-speech detection and offensive behavior in social media are gaining a lot of attention. In this paper, we examine the performance of several supervised classifiers in the process of identifying hate speech on Twitter. More precisely, we do an empirical study that analyzes the influence of two types of linguistic features (n-grams, word embeddings) when they are used to feed different supervised machine learning classifiers: Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Complement Naive Bayes (CNB), Decision Tree (DT), Nearest Neighbors (KN), Random Forest (RF) and Neural Network (NN). The experiments we have carried out show that CNB, SVM, and RF are better than the rest classifiers in English and Spanish languages by taking into account all features.
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Acknowledgments
Research partially funded by the Spanish Ministry of Economy and Competitiveness through projects \(TIN2017-85160-C2-2-R\), and by the Galician Regional Government under projects ED431C 2018/50.
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Almatarneh, S., Gamallo, P., Pena, F.J.R., Alexeev, A. (2019). Supervised Classifiers to Identify Hate Speech on English and Spanish Tweets. In: Jatowt, A., Maeda, A., Syn, S. (eds) Digital Libraries at the Crossroads of Digital Information for the Future. ICADL 2019. Lecture Notes in Computer Science(), vol 11853. Springer, Cham. https://doi.org/10.1007/978-3-030-34058-2_3
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