| 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. |
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Multi-domain Hate Speech Detection Using Dual Contrastive Learning and Paralinguistic Features (2024.lrec-main)
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| Challenge: | a recent study shows that hate speech is spread on social networks and can have social and cultural effects . 41% of americans who took the survey have experienced some type of online harassment . |
| Approach: | They propose a hate speech detection model using contrastive learning loss combined with traditional cross-entropy loss. |
| Outcome: | The proposed model outperforms comparable models on heated topics from two datasets . the model scored macro-F1 on two- and five-class tasks and averaged for four domains compared . |
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. |
Hate Speech Detection Based on Sentiment Knowledge Sharing (2021.acl-long)
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| Challenge: | Existing methods for hate speech detection are stereotyped and biased . et al., a paper examining the effectiveness of multitask learning in hate speech recognition tasks . |
| Approach: | They propose a hate speech detection framework based on sentiment knowledge sharing . they extract affective features of the target sentence and use sentiment features from external resources . |
| Outcome: | The proposed model can detect hate speech over two public datasets. |
Multilingual and Multi-Aspect Hate Speech Analysis (D19-1)
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| Challenge: | Current research on hate speech analysis is oriented towards monolingual and single classification tasks. |
| Approach: | They propose to use a multilingual multi-aspect hate speech analysis dataset to test current methods . they evaluate the dataset in various classification settings and discuss how to leverage annotations . |
| Outcome: | The proposed dataset can be used to improve hate speech detection and classification in general. |
Towards Explainable Hate Speech Detection (2025.findings-acl)
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| Challenge: | Recent advances in deep learning have significantly enhanced the efficiency and accuracy of natural language processing (NLP) tasks. |
| Approach: | They propose a model that uses valence, arousal, and dominance (VAD) scores to detect hate speech and a weighted sum of valent, valance, and valency (VA) scores for classification. |
| Outcome: | The proposed model can compete with state-of-the-art models in detecting hate speech and non-hate speech words based on their individual and summed VAD-values. |
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. |
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. |
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%. |
Exploring Large Language Models for Hate Speech Detection in Rioplatense Spanish (2025.findings-naacl)
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| Challenge: | Hate speech detection deals with many language variants, slang, nuances, and cultural nuances. |
| Approach: | They propose to use large language models to detect hate speech in Rioplatense Spanish . they compare their results to those of a state-of-the-art BERT classifier . |
| Outcome: | The proposed models show lower precision than the state-of-the-art classifier, but are sensitive to highly nuanced cases. |
A Turkish Hate Speech Dataset and Detection System (2022.lrec-1)
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| Challenge: | Davidson et al., 2017: hate speech is a discourse that targets a specific group based on race, gender, religion, sexual orientation, etc. |
| Approach: | They propose a machine learning system for automatic detection of hate speech in Turkish . they use a hate speech dataset and a dataset to collect tweets about immigrants . |
| Outcome: | The proposed system is able to detect hate speech in Turkish and annotate it using BERTurk. |