All You Need is “Love”: Evading Hate Speech Detection
Table 9. F1-scores (macro) from the attack of removing word boundaries and adding “love”.
['[BOLD] Model (data, classes)', '[BOLD] Original', '[BOLD] “Love”']
Based on the word appending results, the performance on character-models would decrease with more appended non-hateful words. Since the original message can relatively easily be recovered, and the single unrelated word has a minimal effect on readability (assuming it to be separable from the rest by the reader), we consider this attack highly successful against state-of-the-art hate speech classification.