Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (N18-2)
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| 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%. |
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Improving Hate Speech Detection by Fusing Textual and User Interaction Representations in Online Communities (2026.acl-industry)
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| Challenge: | Existing studies on toxic content in online communities are limited by the scarcity of data that align textual content with comprehensive social interactions. |
| Approach: | They propose a user-aware hate speech detection framework that effectively fuses textual semantics with social interaction representations to provide pragmatic context for disambiguation. |
| Outcome: | The proposed framework outperforms strong text-only baselines by over 3.6%, validating the critical role of social context in enhancing detection accuracy. |
Directions for NLP Practices Applied to Online Hate Speech Detection (2022.emnlp-main)
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| Challenge: | Existing approaches to address hate speech in online spaces have relied on conventions and practices from NLP. |
| Approach: | They argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task. |
| Outcome: | The proposed methods are poorly suited for the problem and should be adapted to address the propagation of online harms. |
SharedCon: Implicit Hate Speech Detection using Shared Semantics (2024.findings-acl)
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| Challenge: | Recent studies suggest that classifying hateful posts in a binary manner may not address nuanced task of detecting implicit hate speech. |
| Approach: | They propose a contrastive learning approach that leverages shared semantics among data to detect implicit hate speech. |
| Outcome: | The proposed approach is based on a clustering-based contrastive learning approach with human-written implications or machine-generated augmented data. |
Delving into Qualitative Implications of Synthetic Data for Hate Speech Detection (2024.emnlp-main)
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| Challenge: | Recent work on synthetic data for training models for NLP tasks reports mixed results on subjective tasks such as hate speech detection. |
| Approach: | They propose to use synthetic data to train models for highly subjective tasks such as hate speech detection to investigate the potential and specific pitfalls of using it. |
| Outcome: | The proposed model outperforms models trained with real data on hate speech detection tasks, but it fails to accurately reflect real-world data on linguistic dimensions and results in different class distributions. |
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (2020.coling-main)
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| Challenge: | Existing methods to detect online hate speech depend heavily on labeled datasets for training, which results in poor detection performance of the hate speech class. |
| Approach: | They propose a deep generative reinforcement learning model which augments two commonly-used hate speech detection datasets with the HateGAN generated tweets. |
| Outcome: | The proposed model improves the detection performance of hate speech class regardless of the classifiers and datasets used in the detection task. |
Improving Hate Speech Detection with Deep Learning Ensembles (L18-1)
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| Challenge: | censorship is a potential risk when addressing these issues with automated text classification methods. |
| Approach: | They propose to use a neural network-based ensemble method to better classify hate speech using a publicly available embedding model and a popular sentiment dataset. |
| Outcome: | The proposed method improves by 5 points on a hate speech corpus from Twitter and a popular sentiment dataset. |
Label-aware Hard Negative Sampling Strategies with Momentum Contrastive Learning for Implicit Hate Speech Detection (2024.findings-acl)
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| Challenge: | Existing models for implicit hate speech detection do not have significant advantage over cross-entropy loss-based learning. |
| Approach: | They propose a label-aware hard negative sampling strategy that encourages the model to learn detailed features from hard negative samples instead of random batch. |
| Outcome: | The proposed models outperform existing models for implicit hate speech detection both in- and cross-datasets. |
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models (2022.lrec-1)
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| Challenge: | Hate speech detection models are evaluated on a held-out test data, but they are incapable of identifying weaknesses. |
| Approach: | They propose to use multilingual hate speech detection models to evaluate their performance on social media conversation. |
| Outcome: | The proposed model can detect hate speech in multiple languages using a real-world conversation on social media. |
Generalizable Implicit Hate Speech Detection Using Contrastive Learning (2022.coling-1)
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| Challenge: | Hate speech detection is challenging when there are insufficient lexical cues. |
| Approach: | They propose a contrastive learning method that pulls an implication and its corresponding posts close in representation space. |
| Outcome: | The proposed method improves on BERT and HateBERT benchmarks on three implicit hate speech benchmarks. |
A Benchmark Dataset for Learning to Intervene in Online Hate Speech (D19-1)
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| Challenge: | Existing methods to detect online hate speech ignore conversational context . generative hate speech intervention is a novel approach to counter online hate . |
| Approach: | They propose a task where generative hate speech intervention generates responses to intervene during online conversations that contain hate speech. |
| Outcome: | The proposed method can detect and block hate speech and discourage it . it can also generate responses written by Mechanical Turk workers . |