TY  - JOUR
PY  - 2018//
TI  - Automatic detection of cyberbullying in social media text
JO  - PLoS one
A1  - Van Hee, Cynthia
A1  - Jacobs, Gilles
A1  - Emmery, Chris
A1  - Desmet, Bart
A1  - Lefever, Els
A1  - Verhoeven, Ben
A1  - De Pauw, Guy
A1  - Daelemans, Walter
A1  - Hoste, Véronique
SP  - e0203794
EP  - e0203794
VL  - 13
IS  - 10
N2  - 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 F1 score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems.<p />  <p>Language: en</p>
LA  - en
SN  - 1932-6203
UR  - http://dx.doi.org/10.1371/journal.pone.0203794
ID  - ref1
ER  -