Detection and Prevention of Cyberbullying on Social Media

  • Semiu Salawu

Student thesis: Doctoral ThesisDoctor of Philosophy


The Internet and social media have undoubtedly improved our abilities to keep in touch with
friends and loved ones. Additionally, it has opened up new avenues for journalism, activism,
commerce and entertainment. The unbridled ubiquity of social media is, however, not without
negative consequences and one such effect is the increased prevalence of cyberbullying and
online abuse. While cyberbullying was previously restricted to electronic mail, online forums
and text messages, social media has propelled it across the breadth of the Internet,
establishing it as one of the main dangers associated with online interactions. Recent
advances in deep learning algorithms have progressed the state of the art in natural language
processing considerably, and it is now possible to develop Machine Learning (ML) models
with an in-depth understanding of written language and utilise them to detect cyberbullying
and online abuse. Despite these advances, there is a conspicuous lack of real-world
applications for cyberbullying detection and prevention. Scalability; responsiveness;
obsolescence; and acceptability are challenges that researchers must overcome to develop
robust cyberbullying detection and prevention systems.

This research addressed these challenges by developing a novel mobile-based application
system for the detection and prevention of cyberbullying and online abuse. The application
mitigates obsolescence by using different ML models in a “plug and play” manner, thus
providing a mean to incorporate future classifiers. It uses ground truth provided by the enduser to create a personalised ML model for each user. A new large-scale cyberbullying dataset
of over 62K tweets annotated using a taxonomy of different cyberbullying types was created
to facilitate the training of the ML models. Additionally, the design incorporated facilities to
initiate appropriate actions on behalf of the user when cyberbullying events are detected.

To improve the app’s acceptability to the target audience, user-centred design methods were
used to discover stakeholders’ requirements and collaboratively design the mobile app with
young people. Overall, the research showed that (a) the cyberbullying dataset sufficiently
captures different forms of online abuse to allow the detection of cyberbullying and online
abuse; (b) the developed cyberbullying prevention application is highly scalable and
responsive and can cope with the demands of modern social media platforms (b) the use of
user-centred and participatory design approaches improved the app’s acceptability amongst
the target audience.
Date of Award2021
Original languageEnglish
SupervisorJo Lumsden (Supervisor) & Yulan He (Supervisor)


  • cyberbullying detection
  • cyberbullying prevention
  • deep learning
  • participatory design
  • Twitter
  • mobile application

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