The Meta-Sharing Epidemic: Assessing and Mitigating the Multi-Dimensional Risks of Social Media Hyper-Sharing
Not stated
- Funding
- Self-Funded PhD Students Only
- Application deadline
- Year-round applications
About the project
About the Project From daily personal routines to sensitive workplace settings, modern society is driven by an absolute habit: if it happens, it must be digitally shared. Millions of images, short-form videos, and text files are broadcast and forwarded across global communication platforms every single minute. While these frictionless actions feel instant and harmless, this casual habit of sharing "everything and anything" is creating a silent, compounding global crisis. This cutting-edge PhD project moves beyond simple analysis. It is designed around an explicit, two-part research journey to bridge the gap between technical metrics and human behaviour: Algorithmic Discovery & Empirical Validation: Deeply diagnosing the true societal, security, and environmental costs of hyper-sharing. Prototyping & Socio-Technical Interventions: Engineering and testing real-world digital remedies to protect users and the planet. Candidates in this research will investigate the hidden, compounding costs of online media sharing across multiple critical domains: Security & Privacy: Identifying data leaks (such as location markers, biometrics, and background exposures) that fuel automated profiling and identity theft. Environmental Sustainability: Quantifying the invisible carbon footprint and data centre bloat caused by constant high-definition media transit. Socio-Psychological: Mapping the behavioural triggers, algorithmic feedback loops, and erosion of shared peer privacy native to Meta platforms. Political & Economic: Exploring how massive repositories of public media are weaponised for digital exploitation and synthetic media training data. The project is highly interdisciplinary, at the intersection of Computer Science and Human Behavioural Factors, and will leverage Artificial Intelligence (AI) and Machine Learning (ML) technologies, as well as empirical studies, to understand human behaviour in these areas and to develop digital solutions. The successful applicant will join a supportive research community and have opportunities to collaborate with clinical partners, educators, and technology developers. You will gain skills in AI, Sustainability, Cyber Security and Human Computer Interaction. Applicants should have an Honours Degree at 2.1 or above (or equivalent) in Computer Science or related disciplines. In addition, they should have excellent programming skills in Python, statistical tools & techniques and an interest in machine learning and AI.