| 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. |
Similar Papers
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021.emnlp-main)
Copied to clipboard
Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, Diyi Yang
| Challenge: | Existing studies on explicit or overt hate speech have failed to address a more pervasive form based on coded or indirect language. |
| Approach: | They propose a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication. |
| Outcome: | The proposed dataset will serve as a useful benchmark for understanding this multifaceted issue. |
Data-Efficient Methods For Improving Hate Speech Detection (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
| Approach: | They propose an input-level data augmentation technique EasyMix to improve hate speech detection in english and multilingual datasets. |
| Outcome: | The proposed method improves the performance across english and multilingual datasets by 1% and 2-8%. |
Dynamically Refined Regularization for Improving Cross-corpora Hate Speech Detection (2022.findings-acl)
Copied to clipboard
| Challenge: | Hate speech classifiers exhibit performance degradation when evaluated on datasets different from the source. |
| Approach: | They propose to automatically identify and reduce spurious correlations using attribution methods with dynamic refinement of the list of terms that need to be regularized during training. |
| Outcome: | The proposed method improves performance across corpora and on different datasets. |
LLM-Based Multi-Task Bangla Hate Speech Detection: Type, Severity, and Target (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work on social media platforms is limited in its ability to detect hate speech . a lack of reliable and scalable automated hate speech detection systems is a challenge for low-resource languages like Bangla. |
| Approach: | They propose to use a single-task, single-targeted, single language dataset to identify hate speech in Bangla. |
| Outcome: | The proposed dataset is the largest manually annotated Bangla hate-speech dataset to date. |
Delving into Qualitative Implications of Synthetic Data for Hate Speech Detection (2024.emnlp-main)
Copied to clipboard
| 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. |
Deep One-Class Hate Speech Detection Model (2022.lrec-1)
Copied to clipboard
| 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. |
Multilingual and Multi-Aspect Hate Speech Analysis (D19-1)
Copied to clipboard
| 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. |
RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection (2026.acl-long)
Copied to clipboard
| Challenge: | a new framework for hate speech detection addresses implicit hate speech by tailoring the detection process to dataset-specific attributes. |
| Approach: | They propose a framework to account for the dataset-specific characteristics of hate speech datasets. |
| Outcome: | The proposed framework improves detection accuracy and provides interpretable insights into the distinctive features of each dataset. |
Label-aware Hard Negative Sampling Strategies with Momentum Contrastive Learning for Implicit Hate Speech Detection (2024.findings-acl)
Copied to clipboard
| 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. |
Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing approaches for hate-speech detection exhibit poor performance in out-of-domain settings due to overemphasizing source-specific information that negatively impacts its domain invariance. |
| Approach: | They propose a domain adaptation approach that automatically extracts and penalizes source-specific terms using a classifier. |
| Outcome: | The proposed approach improves cross-domain evaluation on indomain held-out instances while preserving high performance on out-of-domain settings. |