ImpliHateVid: A Benchmark Dataset and Two-stage Contrastive Learning Framework for Implicit Hate Speech Detection in Videos (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies on text-based hate speech detection focus on video-based approaches . however, hateful content remains a persistent challenge due to the vast amount of data generated every day. |
| Approach: | They propose a novel two-stage contrastive learning framework for hate speech detection in videos . they train modality-specific encoders for audio, text, and image using contrastive loss . |
| Outcome: | The proposed framework is based on two datasets, ImpliHateVid and HateMM datasets. |
Similar Papers
Generalizable Implicit Hate Speech Detection Using Contrastive Learning (2022.coling-1)
Copied to clipboard
| 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. |
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. |
SharedCon: Implicit Hate Speech Detection using Shared Semantics (2024.findings-acl)
Copied to clipboard
| 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. |
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%. |
More Than Sum of Its Parts: Deciphering Intent Shifts in Multimodal Hate Speech Detection (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing systems struggle with multimodal content where the emergent meaning transcends the aggregation of individual modalities. |
| Approach: | They propose a framework to characterize semantic intent shifts where modalities interact to construct implicit hate from benign cues or neutralize toxicity through semantic inversion. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on H-VLI and on established benchmarks. |
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (2020.coling-main)
Copied to clipboard
| 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. |
Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate (2021.findings-acl)
Copied to clipboard
| Challenge: | Imlicit hate content has unusual syntax, polysemic words, and fewer markers of prejudice, e.g., slurs . multimodal content is harder to detect than unimodal content, such as memes . |
| Approach: | They evaluate the role of semantic and multimodal context for detecting implicit and explicit hate . they find that all models perform better on content with full annotator agreement . |
| Outcome: | The proposed model outperforms other models on implicit and explicit hate detection tasks because of its lower propensity towards false positives. |
AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current approaches to detect hate speech rely on contrastive learning to distinguish hate from non-hate sentences. |
| Approach: | They propose a novel approach to detect implicit hate speech by identifying explicit targets . they use a pretrained Named Entity Recognition model to capture explicit target information . |
| Outcome: | The proposed approach outperforms current methods and achieves faster convergence. |
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. |
A Context-Aware Contrastive Learning Framework for Hateful Meme Detection and Segmentation (2025.findings-naacl)
Copied to clipboard
| Challenge: | Empirical experiments show HateSieve surpasses existing LMMs in performance with fewer trainable parameters . |
| Approach: | They propose a framework to enhance detection and segmentation of hateful elements in memes by creating a triplet dataset and an Image-Text Alignment module. |
| Outcome: | HateSieve features a new framework that creates semantically correlated memes and generates contextual embeddings for accurate meme segmentation. |