Hate Speech and Offensive Language

HSOL is a dataset for hate speech detection. The authors begun with a hate speech lexicon containing words and phrases identified by internet users as hate speech, compiled by Hatebase.org. Using the Twitter API they searched for tweets containing terms from the lexicon, resulting in a sample of tweets from 33,458 Twitter users. They extracted the time-line for each user, resulting in a set of 85.4 million tweets. From this corpus they took a random sample of 25k tweets containing terms from the lexicon and had them manually coded by CrowdFlower (CF) workers. Workers were asked to label each tweet as one of three categories: hate speech, offensive but not hate speech, or neither offensive nor hate speech.

Source: Automated Hate Speech Detection and the Problem of Offensive Language