Using Machine Learning Methods to Study Technology-Facilitated Abuse: Evidence from the Analysis of UK CrimeStoppers’ Text Data

Soldner F., Tanczer L.M., Hammocks D., Lopez-Neira I., Johnson S.D. (2021) Using Machine Learning Methods to Study Technology-Facilitated Abuse: Evidence from the Analysis of UK Crimestoppers’ Text Data. In: Powell A., Flynn A., Sugiura L. (eds) The Palgrave Handbook of Gendered Violence and Technol

25 Pages Posted: 3 Feb 2022

See all articles by Felix Soldner

Felix Soldner

GESIS - Leibniz Institute for the Social Sciences

Leonie Tanczer

UCL Computer Science

Daniel Hammocks

University College London

Isabel Lopez-Neira

University College London

Shane D. Johnson

University College London

Date Written: January 31, 2022

Abstract

Quantitative evidence on technology-facilitated abuse (“tech abuse”) in intimate partner violence (IPV) contexts is lacking globally. This shortcoming creates barriers to the de-velopment of evidence-based interventions. This chapter draws on a data science-driven research project which aims to generate statistical evidence on the nature and extent of IPV tech abuse in the United Kingdom (UK). Using data from the independent UK crime-fighting charity CrimeStoppers (2014-2019), we showcase an automated approach, facilitating Natural Language Processing and machine learning methods, to identify tech abuse cases within large amounts of unstructured text data. The chapter offers both use-ful insights into the types of tech abuse found within the data, as well as the challenges and benefits computational methodologies provide. The research team has released the code and trained machine learning algorithm along with the publication of this chapter. This hopefully allows other researchers to test, deploy, and further improve the automat-ed approach and could facilitate the analysis of other text datasets to identify tech abuse.

Keywords: Machine Learning, Tech Abuse, Technology-Facilitated Abuse, CrimeStoppers

Suggested Citation

Soldner, Felix and Tanczer, Leonie and Hammocks, Daniel and Lopez-Neira, Isabel and Johnson, Shane D., Using Machine Learning Methods to Study Technology-Facilitated Abuse: Evidence from the Analysis of UK CrimeStoppers’ Text Data (January 31, 2022). Soldner F., Tanczer L.M., Hammocks D., Lopez-Neira I., Johnson S.D. (2021) Using Machine Learning Methods to Study Technology-Facilitated Abuse: Evidence from the Analysis of UK Crimestoppers’ Text Data. In: Powell A., Flynn A., Sugiura L. (eds) The Palgrave Handbook of Gendered Violence and Technol, Available at SSRN: https://ssrn.com/abstract=4022708

Felix Soldner

GESIS - Leibniz Institute for the Social Sciences ( email )

Unter Sachsenhausen 6-8, 50667 Köln
Mannheim, 68159
Germany

Leonie Tanczer (Contact Author)

UCL Computer Science ( email )

London
United Kingdom

Daniel Hammocks

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Isabel Lopez-Neira

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Shane D. Johnson

University College London

Gower Street
London, WC1E 6BT
United Kingdom

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