# MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles

arXiv: https://arxiv.org/abs/2402.01967 · HTML: https://mlsanity.com/paper/2402.01967/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection](https://mlsanity.com/paper/2103.01664/) (2021)
- [T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition](https://mlsanity.com/paper/2209.12616/) (2022)
- [MUDES: Multilingual Detection of Offensive Spans](https://mlsanity.com/paper/2102.09665/) (2021)
- [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021)
- [Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts](https://mlsanity.com/paper/2210.03797/) (2022)
- [Offensive Language Identification in Transliterated and Code-Mixed Bangla](https://mlsanity.com/paper/2311.15023/) (2023)
- [Multi-modal Hate Speech Detection using Machine Learning](https://mlsanity.com/paper/2307.11519/) (2023)
- [Hate Me Not: Detecting Hate Inducing Memes in Code Switched Languages](https://mlsanity.com/paper/2204.11356/) (2022)
- [nlpBDpatriots at BLP-2023 Task 1: A Two-Step Classification for Violence Inciting Text Detection in Bangla](https://mlsanity.com/paper/2311.15029/) (2023)

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

- [Large Language Models for Mental Health: A Multilingual Evaluation](https://mlsanity.com/paper/2602.02440/) (2026)
- [Cascading versus Joint Modeling for Hierarchical Offensive Language Detection](https://mlsanity.com/paper/2607.16790/) (2026)

## Abstract (arXiv)

The automatic identification of offensive language such as hate speech is important to keep discussions civil in online communities. Identifying hate speech in multimodal content is a particularly challenging task because offensiveness can be manifested in either words or images or a juxtaposition of the two. This paper presents the MasonPerplexity submission for the Shared Task on Multimodal Hate Speech Event Detection at CASE 2024 at EACL 2024. The task is divided into two sub-tasks: sub-task A focuses on the identification of hate speech and sub-task B focuses on the identification of targets in text-embedded images during political events. We use an XLM-roBERTa-large model for sub-task A and an ensemble approach combining XLM-roBERTa-base, BERTweet-large, and BERT-base for sub-task B. Our approach obtained 0.8347 F1-score in sub-task A and 0.6741 F1-score in sub-task B ranking 3rd on both sub-tasks.