Using Artificial Intelligence to identify perpetrators of technology facilitated coercive control

  • Havard, Tirion (PI)
  • Magill, Chris (CoI)
  • Harvey, Denise (CoI)
  • Bettinson, Vanessa (CoI)

Project Details


This research proposes a feasibility study to test the viability of Natural Language Processing (NLP) and Machine Learning (ML) techniques as tools to identify and risk assess (alleged) perpetrators of Technology Facilitated Coercive Control (TFCC), including those in adolescent relationships.

The project received £115K from Home Office
Funder: Home Office
Collaboration with London South Bank University, De Montfort University and University of Brighton

Key findings

The research shows that abusive traces identified from textual communication can be used to model perpetrators' behaviour
Short titleAI for Domestic Abuse Perpetrators Research
Effective start/end date1/12/2131/05/22

Collaborative partners

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 5 - Gender Equality
  • SDG 16 - Peace, Justice and Strong Institutions


  • sexual violence
  • domestic abuse
  • natural language processing
  • machine learning
  • big data

Research Centres

  • Data and Complex Systems Research Centre

Research Groups

  • SustainNET


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