Academic team: Prof Harith Alani, Dr Elizabeth Cano, Dr Miriam Fernandez,
Policing partners: Dorset Police, Avon and Somerset Police, Lancashire Constabulary
Status: Complete
Online paedophile activity has become a major concern in society with the internet widely available to the general population and young people.
This piece of research looked into whether the different stages of online grooming behaviour could be automatically detected. The proposed approach combines Machine Learning (ML) techniques with existing psychological theories and discourse studies to better encapsulate existing knowledge of online grooming.
The results of this study demonstrate the effectiveness of this approach for the automatic detection of online grooming stages, opening new possibilities for addressing predator grooming behaviour online, and helping policing organisations to act in a preventive way.
| Title | Outputs type | Lead academic | Year |
|---|---|---|---|
| Detecting child grooming behaviour patterns on social media | Executive summary | Alani, H | 2017 |
| Detecting grooming behaviour on social media | Presentation | Alani, H | 2017 |
| Detecting child grooming behaviour patterns on social media | Final report | Alani, H | 2016 |
The UK Government’s Crime and Policing Act received Royal Assent on 29 April 2026, marking a significant development in the legislative response to crime and public safety.
The Act introduces a range of measures aimed at tackling retail crime, shoplifting and anti-social behaviour, alongside stronger penalties for violence against women and girls. It also includes provisions to address cyber-enabled crime and knife crime.