Quantitative Methods for Analyzing Intimate Partner Violence in Microblogs: Observational Study - Homan, Christopher Michael; Schrading, J Nicolas; Ptucha, Raymond W; Cerulli, Catherine; Ovesdotter Alm, Cecilia (JMIR Publications, 2020)
BackgroundSocial 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. ObjectiveThe 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. MethodsHuman 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. ResultsOur 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. ConclusionsOur 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.