BEEP ! K orean Corpus of Online News Comments for Toxic Speech Detection (2020)

Abstract: 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.

## introduction online anonymity provides freedom of speech to many people and lets them speak their opinions in public. however, anonymous speech also has a negative impact on society and individuals @xcite . with anonymity safeguards, individuals easily express hatred against others based on their superficial characteristics such as gender, sexual orientation, and age @xcite . sometimes the hostility leaks to the well-known people who are considered to be the representatives of targeted attributes. recently, korea had suffered a series of tragic incidents of two young celebrities that are presumed to be caused by toxic comments @xcite . since the incidents, two major web portals in korea decided to close the comment system in their entertainment news aggregating service @xcite . even though the toxic comments are now avoidable in those platforms, the fundamental problem has not been solved yet. to cope with the social issue, we propose the first korean corpus annotated for toxic speech detection. specifically, our dataset consists of 9.4k comments from korean online entertainment news articles. each comment is annotated on two aspects, the existence of social bias and hate speech, given that hate speech is closely related to bias @xcite @xcite . considering the context of korean entertainment news where public figures encounter stereotypes mostly intertwined with gender, we weigh more on the prevalent bias. for hate speech, our label categorization refers that of @xcite , namely hate, offensive, and none. the main contributions of this work are as follows: the construction of hate speech corpus has been explored for a limited number of languages, such as english @xcite @xcite , spanish (basile et al., 2019), polish @xcite , portuguese @xcite , and italian @xcite . for korean, works on abusive language have mainly focused on the qualitative discussion of the terminology (hong, 2016), whereas reliable and manual annotation of the corpus has not yet been undertaken. though profanity termbases are currently available 56 , term matching approach frequently makes false predictions (e.g., neologism, polysemy, use-mention distinction), and more importantly, not all hate speech are detectable using such terms @xcite . in addition, hate speech is situated within the context of social bias @xcite . @xcite and @xcite attended to bias in terms of hate speech, however, their interest was mainly in texts that explicitly exhibit sexist or racist terms. in this paper, we consider both explicit and implicit stereotypes, and scrutinize how these are related to hate speech. ## collection we constructed the korean hate speech corpus using the comments from a popular domestic entertainment news aggregation platform. users had been able to leave comments on each article before the recent overhaul @xcite , and we had scrapped the comments from the most-viewed articles. in total, we retrieved 10,403,368 comments from 23,700 articles published from january 1, 2018 to february 29, 2020. we draw 1,580 articles using stratified sampling and extract the top 20 comments ranked in the order of wilson score @xcite on the downvote for each article. then, we remove duplicate comments, single token comments (to eliminate ambiguous ones), and comments composed with more than 100 characters (that could convey various opinions). finally, 10k comments are randomly selected among the rest for annotation. we prepared other 2m comments by gathering the top 100 sorted with the same score for all articles and removed with any overlaps regarding the above 10k comments. this additional corpus is distributed without labels, expected to be useful for pre-training language models on korean online text. ## annotation the annotation was performed by 32 annotators consisting of 29 workers from a crowdsourcing platform deepnatural ai foot_6 and three natural language processing (nlp) researchers. every comment was provided to three random annotators to assign the majority decision. annotators are asked to answer two three-choice questions for each comment: ## conclusions in this data paper, we provide an annotated corpus that can be practically used for analysis and modeling on korean toxic language, including hate speech and social bias. in specific, we construct a corpus of a total of 9.4k comments from online entertainment news service. our dataset has been made publicly accessible with baseline models. we launch kaggle competitions using the corpus, which may facilitate the studies on toxic speech and ameliorate the cyberbullying issues. we hope our initial efforts can be supportive not only to nlp for social good, but also as a useful resource for discerning implicit bias and hate in online languages.