BullStop: A Mobile App for Cyberbullying Prevention

Semiu Salawu, Yulan He, Jo Lumsden


Abstract
Social media has become the new playground for bullies. Young people are now regularly exposed to a wide range of abuse online. In response to the increasing prevalence of cyberbullying, online social networks have increased efforts to clamp down on online abuse but unfortunately, the nature, complexity and sheer volume of cyberbullying means that many cyberbullying incidents go undetected. BullStop is a mobile app for detecting and preventing cyberbullying and online abuse on social media platforms. It uses deep learning models to identify instances of cyberbullying and can automatically initiate actions such as deleting offensive messages and blocking bullies on behalf of the user. Our system not only achieves impressive prediction results but also demonstrates excellent potential for use in real-world scenarios and is freely available on the Google Play Store.
Anthology ID:
2020.coling-demos.13
Volume:
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Michal Ptaszynski, Bartosz Ziolko
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics (ICCL)
Note:
Pages:
70–74
Language:
URL:
https://aclanthology.org/2020.coling-demos.13
DOI:
10.18653/v1/2020.coling-demos.13
Bibkey:
Cite (ACL):
Semiu Salawu, Yulan He, and Jo Lumsden. 2020. BullStop: A Mobile App for Cyberbullying Prevention. In Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations, pages 70–74, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).
Cite (Informal):
BullStop: A Mobile App for Cyberbullying Prevention (Salawu et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-demos.13.pdf