  Hate speech is a major issue in social networks due to the high volume of
data generated daily. Recent works demonstrate the usefulness of machine
learning (ML) in dealing with the nuances required to distinguish between
hateful posts from just sarcasm or offensive language. Many ML solutions for
hate speech detection have been proposed by either changing how features are
extracted from the text or the classification algorithm employed. However, most
works consider only one type of feature extraction and classification
algorithm. This work argues that a combination of multiple feature extraction
techniques and different classification models is needed. We propose a
framework to analyze the relationship between multiple feature extraction and
classification techniques to understand how they complement each other. The
framework is used to select a subset of complementary techniques to compose a
robust multiple classifiers system (MCS) for hate speech detection. The
experimental study considering four hate speech classification datasets
demonstrates that the proposed framework is a promising methodology for
analyzing and designing high-performing MCS for this task. MCS system obtained
using the proposed framework significantly outperforms the combination of all
models 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.
