  Toxic comments in online platforms are an unavoidable social issue under the
cloak of anonymity. Hate speech detection has been actively done for languages
such as English, German, or Italian, where manually labeled corpus has been
released. In this work, we first present 9.4K manually labeled entertainment
news comments for identifying Korean toxic speech, collected from a widely used
online news platform in Korea. The comments are annotated regarding social bias
and hate speech since both aspects are correlated. The inter-annotator
agreement Krippendorff's alpha score is 0.492 and 0.496, respectively. We
provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the
highest score on all tasks. The models generally display better performance on
bias identification, since the hate speech detection is a more subjective
issue. Additionally, when BERT is trained with bias label for hate speech
detection, the prediction score increases, implying that bias and hate are
intertwined. We make our dataset publicly available and open competitions with
the corpus and benchmarks.
