# Detecting weak and strong Islamophobic hate speech on social media

arXiv: https://arxiv.org/abs/1812.10400 · HTML: https://mlsanity.com/paper/1812.10400/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 15 of 15)

- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
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- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- ["Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups](https://mlsanity.com/paper/2102.03870/) (2021)
- [Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines](https://mlsanity.com/paper/2306.14764/) (2023)
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- [LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content](https://mlsanity.com/paper/2407.10995/) (2024)
- [The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse](https://mlsanity.com/paper/2111.10390/) (2021)
- [Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning](https://mlsanity.com/paper/2103.11806/) (2021)
- [HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text](https://mlsanity.com/paper/2403.11108/) (2024)
- [PACO: Provocation Involving Action, Culture, and Oppression](https://mlsanity.com/paper/2303.12808/) (2023)
- [Hostility Detection in UK Politics: A Dataset on Online Abuse Targeting MPs](https://mlsanity.com/paper/2412.04046/) (2024)
- [Dual-Class Prompt Generation: Enhancing Indonesian Gender-Based Hate Speech Detection through Data Augmentation](https://mlsanity.com/paper/2503.04279/) (2025)
- [Decoding Islamophobic Discourse: Using LLMs to Identify Tropes and Semi-Coded Hate Speech](https://mlsanity.com/paper/2503.18273/) (2025)
- [Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobia](https://mlsanity.com/paper/2504.13085/) (2025)

## Abstract (arXiv)

Islamophobic hate speech on social media inflicts considerable harm on both targeted individuals and wider society, and also risks reputational damage for the host platforms. Accordingly, there is a pressing need for robust tools to detect and classify Islamophobic hate speech at scale. Previous research has largely approached the detection of Islamophobic hate speech on social media as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically-informed social science and effectively monitoring social media. Drawing on in-depth conceptual work we build a multi-class classifier which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. We apply the classifier to a dataset of 109,488 tweets produced by far right Twitter accounts during 2017. Whilst most tweets are not Islamophobic, weak Islamophobia is considerably more prevalent (36,963 tweets) than strong (14,895 tweets). Our main input feature is a gloVe word embeddings model trained on a newly collected corpus of 140 million tweets. It outperforms a generic word embeddings model by 5.9 percentage points, demonstrating the importan4ce of context. Unexpectedly, we also find that a one-against-one multi class SVM outperforms a deep learning algorithm.