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Hate speech is a major issue in social networks due to the high volume ofdata generated daily. Recent works demonstrate the usefulness of machinelearning (ML) in dealing with the nuances required to distinguish betweenhateful posts from just sarcasm or offensive language. Many ML solutions forhate speech detection have been proposed by either changing how features areextracted from the text or the classification algorithm employed. However, mostworks consider only one type of feature extraction and classificationalgorithm. This work argues that a combination of multiple feature extractiontechniques and different classification models is needed. We propose aframework to analyze the relationship between multiple feature extraction andclassification techniques to understand how they complement each other. Theframework is used to select a subset of complementary techniques to compose arobust multiple classifiers system (MCS) for hate speech detection. Theexperimental study considering four hate speech classification datasetsdemonstrates that the proposed framework is a promising methodology foranalyzing and designing high-performing MCS for this task. MCS system obtainedusing the proposed framework significantly outperforms the combination of allmodels and the homogeneous and heterogeneous selection heuristics,demonstrating the importance of having a proper selection scheme. Source code,figures, and dataset splits can be found in the GitHub repository:https://github.com/Menelau/Hate-Speech-MCS.

Selecting and combining complementary feature representations and classifiers for hate speech detection