# Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection

arXiv: https://arxiv.org/abs/1804.03124 · HTML: https://mlsanity.com/paper/1804.03124/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2018.

## Built on by (most cited first, 25 of 25)

- [User Preference-aware Fake News Detection](https://mlsanity.com/paper/2104.12259/) (2021)
- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [Handling Bias in Toxic Speech Detection: A Survey](https://mlsanity.com/paper/2202.00126/) (2022)
- [SemEval-2023 Task 10: Explainable Detection of Online Sexism](https://mlsanity.com/paper/2303.04222/) (2023)
- [Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models](https://mlsanity.com/paper/2401.16727/) (2024)
- [How Hate Speech Varies by Target Identity: A Computational Analysis](https://mlsanity.com/paper/2210.10839/) (2022)
- [Lifelong Learning of Hate Speech Classification on Social Media](https://mlsanity.com/paper/2106.02821/) (2021)
- [CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network](https://mlsanity.com/paper/2303.03387/) (2023)
- [You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights](https://mlsanity.com/paper/2108.00524/) (2021)
- [Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate](https://mlsanity.com/paper/2106.05903/) (2021)
- [Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment](https://mlsanity.com/paper/2306.01105/) (2023)
- [Hidden behind the obvious: misleading keywords and implicitly abusive language on social media](https://mlsanity.com/paper/2205.01374/) (2022)
- [Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability](https://mlsanity.com/paper/2103.17191/) (2021)
- [Perceived and Intended Sarcasm Detection with Graph Attention Networks](https://mlsanity.com/paper/2110.04001/) (2021)
- [Enriching Abusive Language Detection with Community Context](https://mlsanity.com/paper/2206.08445/) (2022)
- [Hate Speech Detection via Dual Contrastive Learning](https://mlsanity.com/paper/2307.05578/) (2023)
- [OffensiveLang: A Community Based Implicit Offensive Language Dataset](https://mlsanity.com/paper/2403.02472/) (2024)
- [An Investigation Into Explainable Audio Hate Speech Detection](https://mlsanity.com/paper/2408.06065/) (2024)
- [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)
- [Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks](https://mlsanity.com/paper/2203.02123/) (2022)
- [Improved two-stage hate speech classification for twitter based on Deep Neural Networks](https://mlsanity.com/paper/2206.04162/) (2022)
- [Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic](https://mlsanity.com/paper/2310.08483/) (2023)
- [SoMeR: Multi-View User Representation Learning for Social Media](https://mlsanity.com/paper/2405.05275/) (2024)
- [A Unified Multi-Task Learning Architecture for Hate Detection Leveraging User-Based Information](https://mlsanity.com/paper/2411.06855/) (2024)
- [System Report for CCL25-Eval Task 10: Prompt-Driven Large Language Model Merge for Fine-Grained Chinese Hate Speech Detection](https://mlsanity.com/paper/2512.09563/) (2025)

## Abstract (arXiv)

Hate speech detection is a critical, yet challenging problem in Natural Language Processing (NLP). Despite the existence of numerous studies dedicated to the development of NLP hate speech detection approaches, the accuracy is still poor. The central problem is that social media posts are short and noisy, and most existing hate speech detection solutions take each post as an isolated input instance, which is likely to yield high false positive and negative rates. In this paper, we radically improve automated hate speech detection by presenting a novel model that leverages intra-user and inter-user representation learning for robust hate speech detection on Twitter. In addition to the target Tweet, we collect and analyze the user's historical posts to model intra-user Tweet representations. To suppress the noise in a single Tweet, we also model the similar Tweets posted by all other users with reinforced inter-user representation learning techniques. Experimentally, we show that leveraging these two representations can significantly improve the f-score of a strong bidirectional LSTM baseline model by 10.1%.