Author Profiling for Hate Speech Detection
Table 1: Average weighted precision, recall and f1 scores of the different methods on the Twitter datasest. All improvements are significant (p<0.05) under 10-fold cv paired t-test.
['[EMPTY]', '[BOLD] Method', '[BOLD] p', '[BOLD] r', '[BOLD] f1']
For all three baseline methods (lr, ws, and hs), the addition of author profiling features significantly improves performance (p<0.05 under 10-fold cv paired t-test). The lr + auth method yields the highest performance of f1 =87.57, exceeding its respective baseline by nearly 4 points. A similar trend can be observed for the other methods as well. These results point to the importance of community-based information and author profiling in hate speech detection and demonstrate that our approach can further improve the performance of existing state-of-the-art methods.