ifo Institute - Leibniz Institute for Economic Research at the University of Munich, Munich
This paper asks how social movements against gender-based violence (GBV) spread on Twitter. To this end, I construct a novel dataset measuring 10 large social movements against GBV on Twitter. I show that these movements start suddenly and fade out quickly and that there is considerable variation at the sub-national level in the US. Twitter users are more likely to share content created by other users instead of creating original content. Text mining the text of tweets reveals that polarization is low and that most users express fear and sadness. Neither polarized nor emotional content does generate more traction in form of likes, retweets, replies or quotes. I develop a novel instrumental variable strategy and show that Twitter users with an established network play a major role in the spread of tweets. An analysis of users' profile pictures and names reveals low social inclusiveness of these movements. Users are on average female, young, and White. Tweets posted by non-white users generate less traction. Moreover, women are more prone to reference content by women, while the reverse applies to men.
Economics of gender non-labor discrimination demographic economics public policy social choice clubs committees associations economic sociology