# Abusive Language Detection in Online Conversations by Combining Content-and Graph-based Features

## TL;DR

This paper presents a method that combines message content and conversation structure to improve the automatic detection of abusive language in online chats, achieving high accuracy.

## Contribution

It introduces fusion techniques that integrate content and graph-based features for abusive language detection, enhancing classification performance.

## Key findings

- Achieved an F-measure of 93.26% on abusive message detection.
- Content and conversation dynamics provide complementary information.
- Fusion of features improves detection accuracy.

## Abstract

In recent years, online social networks have allowed worldwide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing amount of messages to check. Methods have been proposed to automatize this moderation process, mainly by providing approaches based on the textual content of the exchanged messages. Recent work has also shown that characteristics derived from the structure of conversations, in the form of conversational graphs, can help detecting these abusive messages. In this paper, we propose to take advantage of both sources of information by proposing fusion methods integrating content-and graph-based features. Our experiments on raw chat logs show that the content of the messages, but also of their dynamics within a conversation contain partially complementary information, allowing performance improvements on an abusive message classification task with a final F-measure of 93.26%.