# Graph-based Features for Automatic Online Abuse Detection

arXiv: https://arxiv.org/abs/1708.01060 · HTML: https://mlsanity.com/paper/1708.01060/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2017.

## Built on by (most cited first, 5 of 5)

- [DeepHate: Hate Speech Detection via Multi-Faceted Text Representations](https://mlsanity.com/paper/2103.11799/) (2021)
- [Session-based Cyberbullying Detection in Social Media: A Survey](https://mlsanity.com/paper/2207.10639/) (2022)
- [Lost-in-Distance: Impact of Contextual Proximity on LLM Performance in Graph Tasks](https://mlsanity.com/paper/2410.01985/) (2024)
- [Cross-lingual hate speech detection based on multilingual domain-specific word embeddings](https://mlsanity.com/paper/2104.14728/) (2021)
- [Enriching Abusive Language Detection with Community Context](https://mlsanity.com/paper/2206.08445/) (2022)

## Abstract (arXiv)

While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach.