# Graph embeddings for Abusive Language Detection

arXiv: https://arxiv.org/abs/2101.02988 · HTML: https://mlsanity.com/paper/2101.02988/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2021.

## Built on by (most cited first, 6 of 6)

- [Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021](https://mlsanity.com/paper/2207.06710/) (2022)
- [Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability](https://mlsanity.com/paper/2103.17191/) (2021)
- [Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights](https://mlsanity.com/paper/2504.01902/) (2025)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)
- [Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning](https://mlsanity.com/paper/2510.17289/) (2025)
- [POLAR:A Per-User Association Test in Embedding Space](https://mlsanity.com/paper/2603.15950/) (2026)

## Abstract (arXiv)

Abusive behaviors are common on online social networks. The increasing frequency of antisocial behaviors forces the hosts of online platforms to find new solutions to address this problem. Automating the moderation process has thus received a lot of interest in the past few years. Various methods have been proposed, most based on the exchanged content, and one relying on the structure and dynamics of the conversation. It has the advantage of being languageindependent, however it leverages a hand-crafted set of topological measures which are computationally expensive and not necessarily suitable to all situations. In the present paper, we propose to use recent graph embedding approaches to automatically learn representations of conversational graphs depicting message exchanges. We compare two categories: node vs. whole-graph embeddings. We experiment with a total of 8 approaches and apply them to a dataset of online messages. We also study more precisely which aspects of the graph structure are leveraged by each approach. Our study shows that the representation produced by certain embeddings captures the information conveyed by specific topological measures, but misses out other aspects.