Publication
Title
Automatic detection of cyberbullying in social media text
Author
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 cyberbullying 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 the task. Experiments on a hold-out test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F-1 score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems.
Language
English
Source (journal)
PLoS ONE
Publication
2018
ISSN
1932-6203
Volume/pages
13 :10 (2018) , p. 1-22
Article Reference
e0203794
ISI
000446632700008
Pubmed ID
30296299
Medium
E-only publicatie
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Project info
Automatic Monitoring for Cyberspace Applications (AMiCA).
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 09.11.2018
Last edited 20.09.2021
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