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:  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, outperforming both ML and DNN baselines .