Citation

Homan CM, Schrading JN, Ptucha RW, Cerulli C, Ovesdotter Alm C. J. Med. Internet. Res. 2020; 22(11): e15347.

Copyright

(Copyright © 2020, Centre for Global eHealth Innovation)

DOI

10.2196/15347

PMID

33211021

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: The aim of this study is to use machine learning and other computational methods to analyze social media data for the reasons victims give for staying in or leaving abusive relationships.

METHODS: Human annotation, part-of-speech tagging, and machine learning predictive models, including support vector machines, were used on a Twitter data set of 8767 #WhyIStayed and #WhyILeft tweets each.

RESULTS: Our methods explored whether we can analyze micronarratives that include details about victims, abusers, and other stakeholders, the actions that constitute abuse, and how the stakeholders respond.

CONCLUSIONS: Our findings are consistent across various machine learning methods, which 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.

Language: en

Keywords

social media; intimate partner violence; natural language processing