Abstract
Quantitative evidence on technology-facilitated abuse (“tech abuse”) in intimate partner violence (IPV) contexts is lacking globally. This shortcoming creates barriers to the development 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 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 useful 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 automated approach and could facilitate the analysis of other text datasets to identify tech abuse.
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Notes
- 1.
University College London (UCL) Research Ethics Committee—Project ID Number: 10503/001.
- 2.
The code and the trained model for making predictions can be found at: “https://osf.io/fea5j/?view_only=35786879fdee4d21bc1da71cba3661d1”.
- 3.
For a full list of all stopwords, see Bird et al. (2009).
- 4.
We also extracted n-gram, and POS proportions, but they resulted in lower classification performances.
- 5.
Model parameters were set to l = 1 and dual = False, while the remaining parameters were unchanged (default settings).
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Acknowledgements
The authors are indebted to Crimestoppers for sharing their data, the Jill Dando Institute Research Laboratory (JDIRL) for hosting all records, and to Oli Hutt and Nigel Swift for providing us with technical support while using the JDIRL. We are also thankful to the book editors (Dr. Asher Flynn, Dr. Anastasia Powell, and Dr. Lisa Sugiura) for their flexibility and support throughout our analysis and write-up process. Parts of the insights discussed in this publication stem from findings derived from UCL’s “Gender and IoT” research project. The latter has received funding from the UCL Social Science Plus+ scheme, UCL Public Policy, the PETRAS IoT Research Hub (EP/N02334X/1), the UK Home Office, and the NEXTLEAP Project (EU Horizon 2020 Framework Programme for Research and Innovation, H2020-ICT-2015, ICT-10-2015, grant agreement No. 688722). The work was also supported by the Dawes Centre for Future Crime at UCL.
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Appendix A: Used Keywords
Appendix A: Used Keywords
Physical devices:
“smart”, “device”, “computer”, “laptop”, “alexa”, “tablet”, “keytracker”, “tracker”, “(smart)-heater”, “light”, “lock”
Online platform apps:
“online”, “technology”, “internet”, “digital”, “dating app”, “facebook”, “systems”, “messages””, “apps”, ““service”, “account”, “platform”, “dating site”, “instagram”, “snapchat”, “tinder”, “app”, “whatsapp”, “spyware”, “find my iPhone”, “find my Friends”, “gps”, “youtube”, “caller id”, “profile”, “sniffer”, “Badoo”, “messenger”, “chat messenger”, “fake account”, “flirtfinder”, “ipad”, “snap chat”, “what’s app”
Verbs:
“dating”, “stalking”, “control”, “victimisation”, “report”, “access”, “texting”, “calling”, “sexting”, “experience”, “bullying”, “rape”, “video”, “use”, “abuse”, “sexualise”, “harass”, “harm”, “perpetrate”, “experiment”, “sharing”, “threat”, “intimate”, “message”, “phone”, “post”, “follow”, “cyberbullying”, “doxing”, “tracking”, “monitoring”, “watching”, “blackmailing”, “humiliate”, “restrict”, “destroy”, “punish”, “force”, “impersonate”, “gaslight”, “controlling”, “distribute”, “hacking”, “attack”, “expose”, “film”, “command”, “spread”, “shout”
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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 Technology. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-83734-1_24
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