# Spread of hate speech in online social media

arXiv: https://arxiv.org/abs/1812.01693 · HTML: https://mlsanity.com/paper/1812.01693/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

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

- [Handling Bias in Toxic Speech Detection: A Survey](https://mlsanity.com/paper/2202.00126/) (2022)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech](https://mlsanity.com/paper/2106.01625/) (2021)
- [Explainable AI: current status and future directions](https://mlsanity.com/paper/2107.07045/) (2021)
- [L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset](https://mlsanity.com/paper/2103.11408/) (2021)
- [Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targets](https://mlsanity.com/paper/2410.07991/) (2024)
- [DeepHate: Hate Speech Detection via Multi-Faceted Text Representations](https://mlsanity.com/paper/2103.11799/) (2021)
- [Hate Speech and Offensive Language Detection in Bengali](https://mlsanity.com/paper/2210.03479/) (2022)
- [MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection](https://mlsanity.com/paper/2401.06526/) (2024)
- [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)
- [AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages](https://mlsanity.com/paper/2501.08284/) (2025)
- [SGHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Singapore](https://mlsanity.com/paper/2405.01842/) (2024)
- ["Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled People](https://mlsanity.com/paper/2410.03448/) (2024)
- [Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan Languages](https://mlsanity.com/paper/2111.13974/) (2021)
- [Causality Guided Disentanglement for Cross-Platform Hate Speech Detection](https://mlsanity.com/paper/2308.02080/) (2023)
- [Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey](https://mlsanity.com/paper/2503.01513/) (2025)
- [Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment](https://mlsanity.com/paper/2306.01105/) (2023)
- [Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach](https://mlsanity.com/paper/2111.14830/) (2021)
- [Retweet communities reveal the main sources of hate speech](https://mlsanity.com/paper/2105.14898/) (2021)
- [On the rise of fear speech in online social media](https://mlsanity.com/paper/2303.10311/) (2023)
- [Low-Resource Counterspeech Generation for Indic Languages: The Case of Bengali and Hindi](https://mlsanity.com/paper/2402.07262/) (2024)
- [Distilling Knowledge from Large Language Models: A Concept Bottleneck Model for Hate and Counter Speech Recognition](https://mlsanity.com/paper/2508.08274/) (2025)
- [Leveraging cross-platform data to improve automated hate speech detection](https://mlsanity.com/paper/2102.04895/) (2021)
- [Unified and Multilingual Author Profiling for Detecting Haters](https://mlsanity.com/paper/2109.09233/) (2021)
- [Hatemongers ride on echo chambers to escalate hate speech diffusion](https://mlsanity.com/paper/2302.02479/) (2023)
- [Perceived and Intended Sarcasm Detection with Graph Attention Networks](https://mlsanity.com/paper/2110.04001/) (2021)
- [Upvotes? Downvotes? No Votes? Understanding the relationship between reaction mechanisms and political discourse on Reddit](https://mlsanity.com/paper/2302.09540/) (2023)
- [Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation](https://mlsanity.com/paper/2310.02876/) (2023)
- [Mind-proofing Your Phone: Navigating the Digital Minefield with GreaseTerminator](https://mlsanity.com/paper/2112.10699/) (2021)

## Abstract (arXiv)

The present online social media platform is afflicted with several issues, with hate speech being on the predominant forefront. The prevalence of online hate speech has fueled horrific real-world hate-crime such as the mass-genocide of Rohingya Muslims, communal violence in Colombo and the recent massacre in the Pittsburgh synagogue. Consequently, It is imperative to understand the diffusion of such hateful content in an online setting. We conduct the first study that analyses the flow and dynamics of posts generated by hateful and non-hateful users on Gab (gab.com) over a massive dataset of 341K users and 21M posts. Our observations confirms that hateful content diffuse farther, wider and faster and have a greater outreach than those of non-hateful users. A deeper inspection into the profiles and network of hateful and non-hateful users reveals that the former are more influential, popular and cohesive. Thus, our research explores the interesting facets of diffusion dynamics of hateful users and broadens our understanding of hate speech in the online world.