Breaking the silence: The effects of online social movements on gender-based violence

This paper investigates the impact of online social movements against gender-based violence (GBV) on GBV-related crime. Using machine learning techniques, we construct a novel dataset tracking the prevalence of GBV-related social media movements on Twitter from 2014 to 2017. Matching this data with weekly FBI crime reports across U.S. states we estimate the effect of tweets related to GBV on GBV-related reported crime. Our econometric approach aims to mitigate concerns of spurious correlations arising from common trends in Twitter usage and crime. We estimate national level regressions with differential effects on GBV-related and non-GBV-related crimes within the same week, as well as regressions at the state-by-week level controlling for state and month fixed effects. Our main findings reveal that GBV-related tweets led to a small but significant short-term decrease in GBV-related crime reports. Further analysis suggests that the decrease is attributable to a reduction in actual crime rather than in reporting behavior.