# DeepHate: Hate Speech Detection via Multi-Faceted Text Representations

arXiv: https://arxiv.org/abs/2103.11799 · HTML: https://mlsanity.com/paper/2103.11799/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 14 Mar 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Visualizing and Understanding Neural Models in NLP](https://mlsanity.com/paper/1506.01066/) (2015)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018)
- [All You Need is "Love": Evading Hate-speech Detection](https://mlsanity.com/paper/1808.09115/) (2018)
- [A C-LSTM Neural Network for Text Classification](https://mlsanity.com/paper/1511.08630/) (2015)
- [One-step and Two-step Classification for Abusive Language Detection on Twitter](https://mlsanity.com/paper/1706.01206/) (2017)
- [Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms](https://mlsanity.com/paper/1801.06482/) (2018)
- [A Unified Deep Learning Architecture for Abuse Detection](https://mlsanity.com/paper/1802.00385/) (2018)
- [Mean Birds: Detecting Aggression and Bullying on Twitter](https://mlsanity.com/paper/1702.06877/) (2017)
- [Graph-based Features for Automatic Online Abuse Detection](https://mlsanity.com/paper/1708.01060/) (2017)

## Built on by (most cited first, 29 of 29)

- [Disentangling Hate in Online Memes](https://mlsanity.com/paper/2108.06207/) (2021)
- [Evaluating GPT-3 Generated Explanations for Hateful Content Moderation](https://mlsanity.com/paper/2305.17680/) (2023)
- [Large-Scale Hate Speech Detection with Cross-Domain Transfer](https://mlsanity.com/paper/2203.01111/) (2022)
- [Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering](https://mlsanity.com/paper/2211.03433/) (2022)
- [ToxiCloakCN: Evaluating Robustness of Offensive Language Detection in Chinese with Cloaking Perturbations](https://mlsanity.com/paper/2406.12223/) (2024)
- [Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study](https://mlsanity.com/paper/2204.01440/) (2022)
- [Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models](https://mlsanity.com/paper/2401.16727/) (2024)
- [Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection](https://mlsanity.com/paper/2106.04681/) (2021)
- [Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation](https://mlsanity.com/paper/2305.13776/) (2023)
- [Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations](https://mlsanity.com/paper/2401.09899/) (2024)
- [Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2303.02513/) (2023)
- [SGHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Singapore](https://mlsanity.com/paper/2405.01842/) (2024)
- [Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection](https://mlsanity.com/paper/2407.21004/) (2024)
- [Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video Detection](https://mlsanity.com/paper/2501.15438/) (2025)
- [Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art](https://mlsanity.com/paper/2308.15745/) (2023)
- [Predicting Hate Intensity of Twitter Conversation Threads](https://mlsanity.com/paper/2206.08406/) (2022)
- [Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment](https://mlsanity.com/paper/2306.01105/) (2023)
- [BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer](https://mlsanity.com/paper/2202.07543/) (2022)
- [Hate Speech and Offensive Language Detection using an Emotion-aware Shared Encoder](https://mlsanity.com/paper/2302.08777/) (2023)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [An Interpretable Approach to Hateful Meme Detection](https://mlsanity.com/paper/2108.10069/) (2021)
- [Enriching Abusive Language Detection with Community Context](https://mlsanity.com/paper/2206.08445/) (2022)
- [Explainable and High-Performance Hate and Offensive Speech Detection](https://mlsanity.com/paper/2206.12983/) (2022)
- [Explainable Identification of Hate Speech towards Islam using Graph Neural Networks](https://mlsanity.com/paper/2311.04916/) (2023)
- [Decentralised Moderation for Interoperable Social Networks: A Conversation-based Approach for Pleroma and the Fediverse](https://mlsanity.com/paper/2404.03048/) (2024)
- [Culture Matters in Toxic Language Detection in Persian](https://mlsanity.com/paper/2506.03458/) (2025)
- [Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore's Low-Resource Languages](https://mlsanity.com/paper/2509.15260/) (2025)
- [Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMA](https://mlsanity.com/paper/2402.06549/) (2024)
- [SyRHM: Symbolic-Language-Enhanced Reasoning with Associative Retrieval for Zero-shot Harmful Meme Detection](https://mlsanity.com/paper/2609.13794/) (2026)

## Abstract (arXiv)

Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many traditional machine learning and deep learning methods to detect hate speech in online social platforms automatically. However, most of these methods have only considered single type textual feature, e.g., term frequency, or using word embeddings. Such approaches neglect the other rich textual information that could be utilized to improve hate speech detection. In this paper, we propose DeepHate, a novel deep learning model that combines multi-faceted text representations such as word embeddings, sentiments, and topical information, to detect hate speech in online social platforms. We conduct extensive experiments and evaluate DeepHate on three large publicly available real-world datasets. Our experiment results show that DeepHate outperforms the state-of-the-art baselines on the hate speech detection task. We also perform case studies to provide insights into the salient features that best aid in detecting hate speech in online social platforms.