Papers with Hate speech detection
Hateful Word in Context Classification (2024.emnlp-main)
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| Challenge: | Hate speech detection is a prevalent research field, yet word meaning is underexplored . lexical cues play a role in determining the hatefulness of words, but are not enough in focus for HSD research. |
| Approach: | They propose a Hateful Word in Context Classification task to determine the hatefulness of a word within a specific context. |
| Outcome: | The proposed task aims to determine the hatefulness of a word within a specific context, and argues that definitions prove effective overall, but not in cases where hateful connotations vary. |
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%. |
Interpretability of LLM Classifiers via the Rational Inattention Theory with Application to Hate Speech Detection (2026.acl-srw)
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| Challenge: | Large language models (LLMs) perform well on text classification, but their decision strategies need to be better understood. |
| Approach: | They propose an extended rational inattention model that parameterizes linguistic noise and information processing cost and provides an interpretable behavioral framework for black-box LLM classifiers. |
| Outcome: | The proposed model provides an interpretable behavioral framework for black-box LLM classifiers. |
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection (2022.emnlp-main)
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Badr AlKhamissi, Faisal Ladhak, Srinivasan Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab
| Challenge: | Hate speech detection is complex and requires commonsense reasoning and social nuance . prior work has shown that even humans cannot achieve a high agreement on whether a post constitutes HS . |
| Approach: | They frame a few-shot learning task to decompose a hate speech detection task into its "constituent" parts. they show that infusing commonsense knowledge from reasoning datasets improves the performance even further. |
| Outcome: | The proposed method outperforms baseline methods in the 16-shot case. |
Unsupervised Embeddings with Graph Auto-Encoders for Multi-domain and Multilingual Hate Speech Detection (2022.lrec-1)
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| Challenge: | Hate speech detection is a challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author. |
| Approach: | They propose an unsupervised approach to learn embeddings for hate speech detection using Graph Auto-Encoders (GAE) they represent texts as nodes of a graph and use transformer layer and convolutional layer to encode them in low-dimensional space. |
| Outcome: | The proposed method shows competitive results on small datasets. |
A Federated Approach for Hate Speech Detection (2023.eacl-main)
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| Challenge: | Despite the scale of social media content, privacy preservation in hate speech detection has remained understudied. |
| Approach: | They propose to use federated machine learning to address privacy concerns in hate speech detection by obtaining a 6.81% improvement in F1-score. |
| Outcome: | The proposed method improves the F1-score of hate speech detection by 6.81% while maintaining public data privacy. |
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. |
Compositional Generalisation for Explainable Hate Speech Detection (2025.emnlp-main)
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| Challenge: | Hate speech detection models struggle to generalise beyond their training data . dataset biases and the use of sentence-level labels fail to teach the underlying structure of hate speech. |
| Approach: | They propose to use a dataset to train models with fine-grained, span-level annotations . they find that combinations of expressions that deviate from those seen during training are difficult to detect . |
| Outcome: | The proposed model can generalise to a dataset with equal frequency across all contexts while achieving state-of-the-art performance on the human-sourced PLEAD. |
Deep One-Class Hate Speech Detection Model (2022.lrec-1)
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| Challenge: | Existing approaches to hate speech detection neglect distinct attributes of hate speeches from other sentimental types such as “aggressive” and “racist”. |
| Approach: | They propose a one-class model where the detection classifier is trained with hate-class samples only. |
| Outcome: | The proposed model outperforms existing models with four benchmark datasets and shows that it performs better than existing models. |
Word-Level Detection of Code-Mixed Hate Speech with Multilingual Domain Transfer (2025.findings-acl)
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| Challenge: | a growing problem in language detection tasks is code-mixing, a combination of more than one language . lack of available datasets for code-mixing causes the problem . authors propose a multilingual approach to code-matching . |
| Approach: | They propose to use an annotated hate speech dataset to detect code-mixing in profane language . they propose to apply bilingual fine-tuned models to code-mixed hate speech in german rap lyrics . |
| Outcome: | The proposed model can detect code-mixed hate speech and neologisms in German rap lyrics . the proposed model is more nuanced than binary classification . |
Uncertainty-Aware Cross-Modal Alignment for Hate Speech Detection (2024.lrec-main)
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| Challenge: | Existing methods for detecting hate speech ignore misalignment and uncertainty between modalities . social media platforms have become conduits for the rapid dissemination of hate speech . |
| Approach: | They propose an uncertainty-aware cross-modal alignment framework for hate speech detection that minimizes the misalignment of image and text in memes. |
| Outcome: | The proposed framework produces a competitive performance compared with existing methods. |