QMUL-NLP at HASOC 2019: Offensive content detection and classification in social media

Abstract

With the development of the Internet, the Web has become an information dissemination platform, an information amplifier, and a new social media. The information load and participation of the Internet far exceeds the existing traditional media, and various problems have emerged. There has been significant work in several languages in partic- ular for English. However, there is a lack of research in this recent and relevant topic for most other languages. This track intends to develop data and evaluation resources for several languages. The objectives are to stimulate research for these languages and to find out the quality of hate speech detection technology in other languages. The paper mainly describes the organization of the HASOC 2019 Task, a Shared Task on Hate Speech and Offensive Content Identification in Indo-European Lan- guages. The task is organized in three related classification subtasks: sub- task A is a coarse-grained binary classification to identify hate speech and offensive language, a fine-grained classification subtask B is to further classify the data from the subtask A into three categories, and subtask C will check the type of offense. This paper mainly focuses on English of- fensive language detection and shows the experimental result in subtask A and subtask B.