ETHOS: an Online Hate Speech Detection Dataset
Table 3. Performance of selected models on binary HS classification
['[EMPTY]', '[BOLD] F1 Score', '[BOLD] F1 Hate', '[BOLD] Accuracy', '[BOLD] Precision', '[BOLD] Sensitivity', '[BOLD] Recall', '[BOLD] Recall Hate', '[BOLD] Specificity', '[BOLD] TN', '[BOLD] FP', '[BOLD] FN', '[BOLD] TP']
Results The handling of textual data is a thoroughly researched task and has a dedicated category, NLP, which stands for natural language processing. We used common and widely accepted techniques to process our textual data, utilising TF-IDF vectorizers, pre-trained embeddings and language models. The best performance per metric is highlighted in bold format. The NNs seem to outperform the conventional ML techniques. It is worth mentioning that DTs, as well as the MNB and BNB, have the lowest performance in terms of almost every metric. Tree-ensembles seem to have similar performance between them, but lower compared to the SVMs and the rest linear models.