dictNN: A Dictionary-Enhanced CNN Approach for Classifying Hate Speech on Twitter
This addresses the problem of detecting evasive hate speech on social media for content moderators, though it appears incremental as it builds on existing CNN methods with dictionary augmentation.
The paper tackled hate speech classification on Twitter by introducing a dictionary-enhanced CNN approach that fuses crowd-sourced hate word vectors with standard embeddings, resulting in a 7 percentage point increase in F1 macro score on a dataset of 110,748 tweets.
Hate speech on social media is a growing concern, and automated methods have so far been sub-par at reliably detecting it. A major challenge lies in the potentially evasive nature of hate speech due to the ambiguity and fast evolution of natural language. To tackle this, we introduce a vectorisation based on a crowd-sourced and continuously updated dictionary of hate words and propose fusing this approach with standard word embedding in order to improve the classification performance of a CNN model. To train and test our model we use a merge of two established datasets (110,748 tweets in total). By adding the dictionary-enhanced input, we are able to increase the CNN model's predictive power and increase the F1 macro score by seven percentage points.