Predicting the Type and Target of Offensive Posts in Social Media.

As offensive content has become pervasive in social media, there has been much research on identifying potentially offensive messages. Previous work in this area, however, did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content. In particular, we propose to model the task hierarchically, identifying the type and the target of offensive messages in social media. We use the Offensive Language Identification Dataset (OLID), a new dataset with a fine-grained three-layer annotation scheme compiled specifically for this purpose. OLID, which we make publicly available, contains tweets annotated for offensive content. We discuss the main similarities and differences of this dataset compared to other datasets for hate speech identification, aggression detection, and similar tasks. We also evaluate the data with a number of classification methods for this task.