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 intimidation. https://www.spidertattooz.com/DND-Gel-Lacquer-Boysenberry-630-p16559/