Predicting the Type and Target of Offensive Posts in Social Media
Table 5: Results for offensive language categorization (level B). We report Precision (P), Recall (R), and F1 for each model/baseline on all classes (TIN, UNT), and weighted averages. Macro-F1 is also listed (best in bold).
['[BOLD] Model', '[BOLD] TIN  [BOLD] P', '[BOLD] TIN  [BOLD] R', '[BOLD] TIN  [BOLD] F1', '[BOLD] UNT  [BOLD] P', '[BOLD] UNT  [BOLD] R', '[BOLD] UNT  [BOLD] F1', '[BOLD] Weighted Average  [BOLD] P', '[BOLD] Weighted Average  [BOLD] R', '[BOLD] Weighted Average  [BOLD] F1', '[BOLD] F1 Macro']
In this set of experiments, the models were trained to discriminate between targeted insults and threats (TIN) and untargeted (UNT) offenses, which generally refer to profanity. We can see that the CNN performs better than the BiLSTM, with a macro-F1 score of 0.69. Note that all models perform better at identifying TIN compared to UNT.