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

Noé Cecillon, Vincent Labatut, Richard Dufour

► To cite this version:

Noé Cecillon, Vincent Labatut, Richard Dufour. Abusive Language Detection in Online Conversations by Combining Content- and Graph-based Features. Meetup LIAvignon, Nov 2022, Avignon, France. 2022. [hal-03857928](https://hal.science/hal-03857928)

HAL Id: hal-03857928

https://hal.science/hal-03857928

Submitted on 17 Nov 2022

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Distributed under a Creative Commons Attribution 4.0 International License
# Abusive Language Detection in Online conversations by Combining Content- and Graph-based Features

Noé Cécillon¹, Vincent Labatut¹, Richard Dufour²

1: LIA - Laboratoire Informatique d'Avignon / 2: LS2N - Laboratoire des Sciences du Numérique de Nantes

## Objective

* Detect abusive messages in online conversations through content- and graph-based methods.

## Approach

1. Exhibit the importance of structural information in the context of online abuse detection.

2. Combine textual and structural information by proposing fusion methods of both sources of information.

## Context

* 62% of french people experienced cyber-harassment at least once.

* Companies are responsible for content posted on their platforms.

* Billion daily users and messages sent on social networks.

* Moderation implies high human and financial costs.

Our proposition to automate the detection of abusive content online :

* A method based on a mix of standard NLP features.

* A graph-based approach that relies on interaction and completely ignores the textual content of messages.

* A combination of both approaches.

## Content-based method

We extract NLP features from the content of each considered message to train a SVM classifier to distinguish between *Abuse* and *Non-abuse* class.

<figure><img src="image_3.png" /></figure>

Content-based pipeline. NLP features include the count of unique characters in the message, classes of characters, proportion of capital letters and numbers.

A major limitation of the content-based methods is their sensitivity to intentional text obfuscation. Example :

**Pi&ce 0f sh1t, D4mn, f*ck y**, bul..hit

## Conversational graph extraction

* Extraction of a conversational graph from a sequence of messages.

* Vertices represent users and edges model interactions between them.

<figure><img src="image_4.png" /><figcaption>A sequence of messages and the corresponding graph.</figcaption></figure>

## Graph-based method

We extract a number of topological measures to describe the conversational graph.

A SVM classifier is then trained using these values as features.

<figure><img src="image_5.png" /></figure>

Graph-based pipeline. topological measures include density, edge count, degree centrality, eccentricity...

## Fusion

We propose a method seeking to take advantage of both previous ones by combining them.

<figure><img src="image_6.png" /></figure>

Graph- and Content-based methods combined through a third SVM.

## Results

* Graph-based method performs better than Content-based method.

* Fusion method obtains better results than them

→ Graph- and Content-based methods are complementary

<table><thead><tr><td>Method</td><td>Number of features</td><td>Precision</td><td>Recall</td><td>F-measure</td></tr></thead><tbody><tr><td>Content-Based</td><td>29</td><td>78.59</td><td>83.61</td><td>81.02</td></tr><tr><td>Graph-Based</td><td>459</td><td>90.21</td><td>87.63</td><td>88.90</td></tr><tr><td>Fusion</td><td>488 (2)</td><td>94.10</td><td>92.43</td><td>93.26</td></tr></tbody></table>

Comparison of the performances obtained with the methods Content-based, Graph-based and their Fusion.

The dataset is extracted from the in-game chat of an online video-game.

## Perspectives

* Replace NLP features extraction with text embedding.

* Replace graph topological measures with whole graph embedding.

<figure><img src="image_7.png" /></figure>

CV HAL

<figure><img src="image_8.png" /><figcaption>Noé Cécillon
noe.cecillon@univ-avignon.fr</figcaption></figure>

PhD student in Computer Science at Avignon University.
Main research interests :

* Representation learning

* Natural Language Processing