Before revision:  Evaluations against several machine learning ~ (linear and tree-based models) and deep neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1 scores of 84\%, 90\%, 88\%, and 88\%, for political, personal, geopolitical, and religious hates, respectively, during 3-fold cross-validation tests .
After revision:  Finally, to measure the quality of the explanation (i.e., faithfulness), we compute the comprehensiveness and sufficiency. Evaluations against machine learning (linear and tree-based models) and deep neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1 scores of 84\%, 90\%, 88\%, and 88\%, for political, personal, geopolitical, and religious hates, respectively, during 3-fold cross-validation tests .