Predicting the Type and Target of Offensive Posts in Social Media
Table 4: Results for offensive language detection (Level A). We report Precision (P), Recall (R), and F1 for each model/baseline on all classes (NOT, OFF), and weighted averages. Macro-F1 is also listed (best in bold).
['[BOLD] Model', '[BOLD] NOT  [BOLD] P', '[BOLD] NOT  [BOLD] R', '[BOLD] NOT  [BOLD] F1', '[BOLD] OFF  [BOLD] P', '[BOLD] OFF  [BOLD] R', '[BOLD] OFF  [BOLD] F1', '[BOLD] Weighted Average  [BOLD] P', '[BOLD] Weighted Average  [BOLD] R', '[BOLD] Weighted Average  [BOLD] F1', '[BOLD] F1 Macro']
We can see that all models perform significantly better than chance, with the neural models performing substantially better than the SVM. The CNN outperforms the RNN model, achieving a macro-F1 score of 0.80.