Automatic Detection of Cyberbullying in Social Media Text

TL;DR

This paper presents a machine learning approach for automatically detecting cyberbullying in social media posts, using a new annotated corpus in English and Dutch, achieving promising results that outperform baseline methods.

Contribution

It introduces a new annotated dataset for cyberbullying detection in English and Dutch and evaluates the effectiveness of linear SVM classifiers with rich features.

Findings

F1-score of 64% for English and 61% for Dutch

Outperforms keyword-based baseline systems

Demonstrates feasibility of automatic cyberbullying detection

Abstract

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a training corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for this particular task. Experiments on a holdout test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.