# Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media

arXiv: https://arxiv.org/abs/1804.04257 · HTML: https://mlsanity.com/paper/1804.04257/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 30 of 59)

- [Hate Speech and Counter Speech Detection: Conversational Context Does Matter](https://mlsanity.com/paper/2206.06423/) (2022)
- [From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models](https://mlsanity.com/paper/2305.17174/) (2023)
- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- [How Hate Speech Varies by Target Identity: A Computational Analysis](https://mlsanity.com/paper/2210.10839/) (2022)
- [An Investigation of Large Language Models for Real-World Hate Speech Detection](https://mlsanity.com/paper/2401.03346/) (2024)
- [AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection](https://mlsanity.com/paper/2103.11800/) (2021)
- [Towards Interpretable Hate Speech Detection using Large Language Model-extracted Rationales](https://mlsanity.com/paper/2403.12403/) (2024)
- [Multimodal Hate Speech Detection from Bengali Memes and Texts](https://mlsanity.com/paper/2204.10196/) (2022)
- [MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection](https://mlsanity.com/paper/2401.06526/) (2024)
- ["Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups](https://mlsanity.com/paper/2102.03870/) (2021)
- [Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language](https://mlsanity.com/paper/2109.10255/) (2021)
- [You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights](https://mlsanity.com/paper/2108.00524/) (2021)
- [Interpretable Multi-Modal Hate Speech Detection](https://mlsanity.com/paper/2103.01616/) (2021)
- [DetoxLLM: A Framework for Detoxification with Explanations](https://mlsanity.com/paper/2402.15951/) (2024)
- [Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions](https://mlsanity.com/paper/2101.11956/) (2021)
- [Causality Guided Disentanglement for Cross-Platform Hate Speech Detection](https://mlsanity.com/paper/2308.02080/) (2023)
- [ToXCL: A Unified Framework for Toxic Speech Detection and Explanation](https://mlsanity.com/paper/2403.16685/) (2024)
- [Hate Speech Detection with Generalizable Target-aware Fairness](https://mlsanity.com/paper/2406.00046/) (2024)
- [PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework](https://mlsanity.com/paper/2306.08804/) (2023)
- [The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets](https://mlsanity.com/paper/2405.08562/) (2024)
- [Decoding Hate: Exploring Language Models' Reactions to Hate Speech](https://mlsanity.com/paper/2410.00775/) (2024)
- [Towards Efficient and Explainable Hate Speech Detection via Model Distillation](https://mlsanity.com/paper/2412.13698/) (2024)
- [Cross-lingual hate speech detection based on multilingual domain-specific word embeddings](https://mlsanity.com/paper/2104.14728/) (2021)
- [Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning](https://mlsanity.com/paper/2209.10193/) (2022)
- [Rationale-Guided Few-Shot Classification to Detect Abusive Language](https://mlsanity.com/paper/2211.17046/) (2022)
- [SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection](https://mlsanity.com/paper/2409.16673/) (2024)
- [Advancing Hate Speech Detection with Transformers: Insights from the MetaHate](https://mlsanity.com/paper/2508.04913/) (2025)
- [AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context](https://mlsanity.com/paper/2410.16520/) (2024)
- [Social Support Detection from Social Media Texts](https://mlsanity.com/paper/2411.02580/) (2024)
- [Efficient Hate Speech Detection: Evaluating 38 Models from Traditional Methods to Transformers](https://mlsanity.com/paper/2509.14266/) (2025)

## Abstract (arXiv)

While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech -- its target: either "directed" towards a specific person or entity, or "generalized" towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech and reveal the presence of interesting markers that distinguish these types of hate speech. Our analysis reveals that Directed hate speech, in addition to being more personal and directed, is more informal, angrier, and often explicitly attacks the target (via name calling) with fewer analytic words and more words suggesting authority and influence. Generalized hate speech, on the other hand, is dominated by religious hate, is characterized by the use of lethal words such as murder, exterminate, and kill; and quantity words such as million and many. Altogether, our work provides a data-driven analysis of the nuances of online-hate speech that enables not only a deepened understanding of hate speech and its social implications but also its detection.