# QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech Classification

arXiv: https://arxiv.org/abs/2012.08312 · HTML: https://mlsanity.com/paper/2012.08312/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [MHS-STMA: Multimodal Hate Speech Detection via Scalable Transformer-Based Multilevel Attention Framework](https://mlsanity.com/paper/2409.05136/) (2024)
- [Layer-Aware Embedding Fusion for LLMs in Text Classifications](https://mlsanity.com/paper/2504.05764/) (2025)

## Abstract (arXiv)

Hate speech, quite common in the age of social media, at times harmless but can also cause mental trauma to someone or even riots in communities. Image of a religious symbol with derogatory comment or video of a man abusing a particular community, all become hate speech with its every modality (such as text, image, and audio) contributing towards it. Models based on a particular modality of hate speech post on social media are not useful, rather, we need models like multi-modal fusion models that consider both image and text while classifying hate speech. Text-image fusion models are heavily parameterized, hence we propose a quaternion neural network-based model having additional fusion components for each pair of modalities. The model is tested on the MMHS150K twitter dataset for hate speech classification. The model shows an almost 75% reduction in parameters and also benefits us in terms of storage space and training time while being at par in terms of performance as compared to its real counterpart.