JL-Hate: An Annotated Dataset for Joint Learning of Hate Speech and Target Detection (2024.lrec-main)
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| Challenge: | Existing data resources for the detection of hate speech focus on text sequence classification, but the target of hateful content is lacking. |
| Approach: | They propose a tweet dataset for the task of joint learning of hate speech detection and target detection called JL-Hate. |
| Outcome: | The proposed dataset performs similar tasks to the existing datasets in sequence and token classification tasks. |
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| Challenge: | Social media enables widespread propagation of hate speech targeting groups based on ethnicity, religion, or other characteristics. |
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| Challenge: | Current research on hate speech analysis is oriented towards monolingual and single classification tasks. |
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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. |
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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. |
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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. |
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| Challenge: | a recent study shows that many definitions are being used for equivalent concepts, making most datasets incompatible. |
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Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages (2022.emnlp-main)
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| Challenge: | Hate speech datasets focus on English-language content, hindering effective models . annotating hateful content is expensive, time-consuming and potentially harmful to annotators. |
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K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific Ratings (2023.findings-emnlp)
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| Challenge: | Existing datasets on hate speech detection focus on overt forms of hate . however, a majority of these resources are English-centric, focusing on overtones of hate. |
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Data-Efficient Methods For Improving Hate Speech Detection (2023.findings-eacl)
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| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
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Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data (2025.emnlp-main)
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| Challenge: | Existing methods for detecting hate speech data are expensive and time-consuming . labeled data is expensive and difficult to collect, especially for low-resource languages . |
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