Papers with automated hate speech detection
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (N18-2)
Copied to clipboard
| Challenge: | Existing methods that focus on a single tweet as input are likely to yield high false positive and negative rates. |
| Approach: | They propose a model that leverages intra-user and inter-user representation learning to improve hate speech detection on Twitter by suppressing the noise in a single Tweet. |
| Outcome: | The proposed model significantly improves the f-score of a strong bidirectional LSTM model by 10.1%. |
Hierarchical CVAE for Fine-Grained Hate Speech Classification (D18-1)
Copied to clipboard
| Challenge: | Existing work on automated hate speech detection focuses on binary classification or on differentiating among a small set of categories. |
| Approach: | They propose a method to discriminate among 40 hate groups of 13 different hate group categories. |
| Outcome: | The proposed method outperforms discriminative models on a fine-grained hate speech classification task. |