WhyIStayed, #WhyILeft: Microblogging to make sense of domestic abuse

Abstract

In September 2014, Twitter users unequivocally reacted to the Ray Rice assault scandal by unleashing personal stories of domestic abuse via the hashtags #WhyIStayed or #WhyILeft. We explore at a macro-level firsthand accounts of domestic abuse from a substantial, balanced corpus of tweeted instances designated with these tags. To seek insights into the reasons victims give for staying in vs. leaving abusive relationships, we analyze the corpus using linguistically motivated methods. We also report on an annotation study for corpus assessment. We perform classification, contributing a classifier that discriminates between the two hashtags exceptionally well at 82% accuracy with a substantial error reduction over its baseline.