Ideological asymmetries in online hostility, intimidation, obscenity, and prejudice
Abstract To investigate ideological symmetries and asymmetries in the expression of online prejudice, we used machine-learning methods to estimate the prevalence of extreme hostility in a large dataset of Twitter messages harvested in 2016.We analyzed language contained in 730,000 tweets on the following dimensions of bias: (1) threat and intimidat