# Online Hate: Behavioural Dynamics and Relationship with Misinformation

arXiv: https://arxiv.org/abs/2105.14005 · HTML: https://mlsanity.com/paper/2105.14005/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Community Interaction and Conflict on the Web](https://mlsanity.com/paper/1803.03697/) (2018)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [Multilingual Twitter Sentiment Classification: The Role of Human Annotators](https://mlsanity.com/paper/1602.07563/) (2016)
- [Debunking in a World of Tribes](https://mlsanity.com/paper/1510.04267/) (2015)
- [Emotional Dynamics in the Age of Misinformation](https://mlsanity.com/paper/1505.08001/) (2015)
- [The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English](https://mlsanity.com/paper/1906.02045/) (2019)
- ["Go eat a bat, Chang!": On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19](https://mlsanity.com/paper/2004.04046/) (2020)

## Built on by (most cited first, 3 of 3)

- [GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates](https://mlsanity.com/paper/2202.08175/) (2022)
- [A Graph-Based Context-Aware Model to Understand Online Conversations](https://mlsanity.com/paper/2211.09207/) (2022)
- [GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding](https://mlsanity.com/paper/2310.14028/) (2023)

## Abstract (arXiv)

Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of "serial haters", intended as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of number of comments and time. Our results show that, coherently with Godwin's law, online debates tend to degenerate towards increasingly toxic exchanges of views.