Misogynistic and Xenophobic Hate Language Online: A Matter of Anonymity

In this paper, we quantify hateful content in online civic discussions of politics and estimate the causal link between hateful content and writer anonymity. To measure hate, we first develop a supervised machine-learning model that predicts hate against foreign residents and hate against women on a dominant Swedish Internet discussion forum. We find that an exogenous decrease in writer anonymity leads to less hate against foreign residents but an increase in hate against women. We conjecture that the mechanisms behind the changes comprise a combination of users decreasing the amount of their hateful writing and a substitution of hate against foreign residents for hate against women. The discussion of the results highlights the role of social repercussions in discouraging antisocial and criminal activities.