# ImpliHateVid: A Benchmark Dataset and Two-stage Contrastive Learning Framework for Implicit Hate Speech Detection in Videos

arXiv: https://arxiv.org/abs/2508.06570 · HTML: https://mlsanity.com/paper/2508.06570/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://mlsanity.com/paper/2203.09509/) (2022)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [Prompting for Multimodal Hateful Meme Classification](https://mlsanity.com/paper/2302.04156/) (2023)
- [Detecting and Understanding Harmful Memes: A Survey](https://mlsanity.com/paper/2205.04274/) (2022)
- [Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection](https://mlsanity.com/paper/2308.08088/) (2023)
- [HateMM: A Multi-Modal Dataset for Hate Video Classification](https://mlsanity.com/paper/2305.03915/) (2023)
- [Decoding the Underlying Meaning of Multimodal Hateful Memes](https://mlsanity.com/paper/2305.17678/) (2023)
- [MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili](https://mlsanity.com/paper/2408.03468/) (2024)

## Built on by (most cited first, 12 of 12)

- [D-HUMOR: Dark Humor Understanding via Multimodal Open-ended Reasoning -- A Benchmark Dataset and Method](https://mlsanity.com/paper/2509.06771/) (2025)
- [A Multimodal-Multitask Framework with Cross-modal Relation and Hierarchical Interactive Attention for Semantic Comprehension](https://mlsanity.com/paper/2508.16300/) (2025)
- [StreamSense: Streaming Social Task Detection with Selective Vision-Language Model Routing](https://mlsanity.com/paper/2601.22738/) (2026)
- [A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction](https://mlsanity.com/paper/2509.15966/) (2025)
- [MultiHateLoc: Towards Temporal Localisation of Multimodal Hate Content in Online Videos](https://mlsanity.com/paper/2512.10408/) (2025)
- [X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and A Novel LLM-consulted Explanation Framework](https://mlsanity.com/paper/2601.03194/) (2026)
- [Two Stage Context Learning with Large Language Models for Multimodal Stance Detection on Climate Change](https://mlsanity.com/paper/2509.08024/) (2025)
- [RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection](https://mlsanity.com/paper/2510.23648/) (2025)
- [Reasoning-Aware Multimodal Fusion for Hateful Video Detection](https://mlsanity.com/paper/2512.02743/) (2025)
- [TANDEM: Temporal-Aware Neural Detection for Multimodal Hate Speech](https://mlsanity.com/paper/2601.11178/) (2026)
- [When Jokes Cross the Line: Analyzing Regular Humor and Dark Humor in YouTube Shorts](https://mlsanity.com/paper/2606.00046/) (2026)
- [Decoding Multimodal Cues: Unveiling the Implicit Meaning Behind Hateful Videos](https://mlsanity.com/paper/2606.11953/) (2026)

## Abstract (arXiv)

The existing research has primarily focused on text and image-based hate speech detection, video-based approaches remain underexplored. In this work, we introduce a novel dataset, ImpliHateVid, specifically curated for implicit hate speech detection in videos. ImpliHateVid consists of 2,009 videos comprising 509 implicit hate videos, 500 explicit hate videos, and 1,000 non-hate videos, making it one of the first large-scale video datasets dedicated to implicit hate detection. We also propose a novel two-stage contrastive learning framework for hate speech detection in videos. In the first stage, we train modality-specific encoders for audio, text, and image using contrastive loss by concatenating features from the three encoders. In the second stage, we train cross-encoders using contrastive learning to refine multimodal representations. Additionally, we incorporate sentiment, emotion, and caption-based features to enhance implicit hate detection. We evaluate our method on two datasets, ImpliHateVid for implicit hate speech detection and another dataset for general hate speech detection in videos, HateMM dataset, demonstrating the effectiveness of the proposed multimodal contrastive learning for hateful content detection in videos and the significance of our dataset.