A System to Monitor Cyberbullying based on Message Classification and Social Network Analysis

Stefano Menini, Giovanni Moretti, Michele Corazza, Elena Cabrio, Sara Tonelli, Serena Villata


Abstract
Social media platforms like Twitter and Instagram face a surge in cyberbullying phenomena against young users and need to develop scalable computational methods to limit the negative consequences of this kind of abuse. Despite the number of approaches recently proposed in the Natural Language Processing (NLP) research area for detecting different forms of abusive language, the issue of identifying cyberbullying phenomena at scale is still an unsolved problem. This is because of the need to couple abusive language detection on textual message with network analysis, so that repeated attacks against the same person can be identified. In this paper, we present a system to monitor cyberbullying phenomena by combining message classification and social network analysis. We evaluate the classification module on a data set built on Instagram messages, and we describe the cyberbullying monitoring user interface.
Anthology ID:
W19-3511
Volume:
Proceedings of the Third Workshop on Abusive Language Online
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | ALW | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
105–110
Language:
URL:
https://www.aclweb.org/anthology/W19-3511
DOI:
10.18653/v1/W19-3511
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3511.pdf