Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jul 2, 2019
Open Peer Review Period: Jul 8, 2019 - Sep 2, 2019
Date Accepted: Jul 26, 2020
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Quantitative Methods for Analyzing Intimate Partner Violence in Social Media
ABSTRACT
Background:
Social media is a rich, virtually untapped source of data on the dynamics of intimate partner violence, one that is both global in scale and intimate in detail.
Objective:
To use a variety of computational methods to discover from social media data the reasons victims give for staying in or leaving abusive relationships.
Methods:
Human annotation, part-of-speech tagging, generative language modeling, machine learning predictive models---including support vector machines, long-short-term memory, on a Twitter dataset of 8,767 #WhyIStayed and #WhyILeft tweets each.
Results:
Our methods reveal micro-narratives that include details about victims, abusers, and other stakeholders, the actions that constitute abuse, and how the stakeholders respond. Additionally, we demonstrate an application of encoder-decoder models: to generate synthetic tweets that retain the qualities of actual tweets, but due to their synthetic nature provide a measure of anonymity.
Conclusions:
Our findings are consistent across various machine learning methods, correspond to observations in the clinical literature, and affirm the relevance of natural language processing and machine learning for exploring issues of societal importance in social media. Clinical Trial: No clinical trial.
Citation
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Copyright
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