# Multi-modal Hate Speech Detection using Machine Learning

arXiv: https://arxiv.org/abs/2307.11519 · HTML: https://mlsanity.com/paper/2307.11519/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [Predictive Embeddings for Hate Speech Detection on Twitter](https://mlsanity.com/paper/1809.10644/) (2018)

## Built on by (most cited first, 8 of 8)

- [Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models](https://mlsanity.com/paper/2401.16727/) (2024)
- [Efficient Hate Speech Detection: Evaluating 38 Models from Traditional Methods to Transformers](https://mlsanity.com/paper/2509.14266/) (2025)
- [ARC-NLP at Multimodal Hate Speech Event Detection 2023: Multimodal Methods Boosted by Ensemble Learning, Syntactical and Entity Features](https://mlsanity.com/paper/2307.13829/) (2023)
- [MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles](https://mlsanity.com/paper/2402.01967/) (2024)
- [Trustworthy Hate Speech Detection Through Visual Augmentation](https://mlsanity.com/paper/2409.13557/) (2024)
- [STEMTOX: From Collaborative Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning](https://mlsanity.com/paper/2508.04166/) (2025)
- [Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach](https://mlsanity.com/paper/2604.08609/) (2026)
- [Efficient Adaptation of LLMs for Hate Speech Detection in Low-Resource Languages: A Comparative Study on Roman Urdu](https://mlsanity.com/paper/2608.18142/) (2026)

## Abstract (arXiv)

With the continuous growth of internet users and media content, it is very hard to track down hateful speech in audio and video. Converting video or audio into text does not detect hate speech accurately as human sometimes uses hateful words as humorous or pleasant in sense and also uses different voice tones or show different action in the video. The state-ofthe-art hate speech detection models were mostly developed on a single modality. In this research, a combined approach of multimodal system has been proposed to detect hate speech from video contents by extracting feature images, feature values extracted from the audio, text and used machine learning and Natural language processing.