# Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech

arXiv: https://arxiv.org/abs/2201.06723 · HTML: https://mlsanity.com/paper/2201.06723/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [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)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech](https://mlsanity.com/paper/1910.03270/) (2019)
- [Arabic Dialect Identification in the Wild](https://mlsanity.com/paper/2005.06557/) (2020)
- [Detecting Propaganda Techniques in Memes](https://mlsanity.com/paper/2109.08013/) (2021)
- [Pre-Training BERT on Arabic Tweets: Practical Considerations](https://mlsanity.com/paper/2102.10684/) (2021)
- [Arabic Offensive Language on Twitter: Analysis and Experiments](https://mlsanity.com/paper/2004.02192/) (2020)
- [OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach](https://mlsanity.com/paper/2005.07297/) (2020)
- [Seminar Users in the Arabic Twitter Sphere](https://mlsanity.com/paper/1707.07276/) (2017)
- [Cross-lingual Emotion Detection](https://mlsanity.com/paper/2106.06017/) (2021)

## Built on by (most cited first, 14 of 14)

- [Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis](https://mlsanity.com/paper/2308.16705/) (2023)
- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- [KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application](https://mlsanity.com/paper/2305.17701/) (2023)
- [Arabic Dataset for LLM Safeguard Evaluation](https://mlsanity.com/paper/2410.17040/) (2024)
- [Multilingual Content Moderation: A Case Study on Reddit](https://mlsanity.com/paper/2302.09618/) (2023)
- [Multi3Hate: Multimodal, Multilingual, and Multicultural Hate Speech Detection with Vision-Language Models](https://mlsanity.com/paper/2411.03888/) (2024)
- [Offensive Hebrew Corpus and Detection using BERT](https://mlsanity.com/paper/2309.02724/) (2023)
- [AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify](https://mlsanity.com/paper/2207.08557/) (2022)
- [Toxic language detection: a systematic review of Arabic datasets](https://mlsanity.com/paper/2312.07228/) (2023)
- [Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification](https://mlsanity.com/paper/2205.07960/) (2022)
- [A Benchmark Study of Contrastive Learning for Arabic Social Meaning](https://mlsanity.com/paper/2210.12314/) (2022)
- [Transformers and Ensemble methods: A solution for Hate Speech Detection in Arabic languages](https://mlsanity.com/paper/2303.09823/) (2023)
- [Impact of emoji exclusion on the performance of Arabic sarcasm detection models](https://mlsanity.com/paper/2405.02195/) (2024)
- [Multi-task Learning with Active Learning for Arabic Offensive Speech Detection](https://mlsanity.com/paper/2506.02753/) (2025)

## Abstract (arXiv)

We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to collect a large number of offensive tweets. We apply the proposed method on Arabic tweets and compare it with English tweets - analysing key cultural differences. We observed a constant usage of these emojis to represent offensiveness throughout different timespans on Twitter. We manually annotate and publicly release the largest Arabic dataset for offensive, fine-grained hate speech, vulgar and violence content. Furthermore, we benchmark the dataset for detecting offensiveness and hate speech using different transformer architectures and perform in-depth linguistic analysis. We evaluate our models on external datasets - a Twitter dataset collected using a completely different method, and a multi-platform dataset containing comments from Twitter, YouTube and Facebook, for assessing generalization capability. Competitive results on these datasets suggest that the data collected using our method captures universal characteristics of offensive language. Our findings also highlight the common words used in offensive communications, common targets for hate speech, specific patterns in violence tweets; and pinpoint common classification errors that can be attributed to limitations of NLP models. We observe that even state-of-the-art transformer models may fail to take into account culture, background and context or understand nuances present in real-world data such as sarcasm.