BullStop: A Mobile App for Cyberbullying Prevention

Semiu Salawu*, Yulan He, Jo Lumsden

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputChapter (peer-reviewed)peer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 28th International Conference on Computational Linguistics
Subtitle of host publicationSystem Demonstrations
PublisherAssociation for Computational Linguistics
Number of pages5
ISBN (Print)9781952148286
Publication statusPublished - Dec 2020
EventThe 28th International Conference on Computational Linguistics - Barcelona, Spain
Duration: 8 Dec 202013 Dec 2020


ConferenceThe 28th International Conference on Computational Linguistics
Abbreviated titleCOLING 2020
Internet address

Bibliographical note

This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: https://creativecommons.org/licenses/by/4.0/


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