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BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection

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BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection Jihyung Moon*,y,1, Won Ik Cho*,2, Junbum Lee3 Department of Industrial Engineering1, Department of Electrical and Computer Engineering and INMC2, Graduate School of Data Science3, Seoul National University, Seoul fans1107,tsatsuki,[email protected] Abstract Recently, Korea had suffered a series of tragic in- cidents of two young celebrities that are presumed Toxic comments in online platforms are an to be caused by toxic comments (Fortin, 2019; Mc- unavoidable social issue under the cloak of Curry, 2019a,b). Since the incidents, two major anonymity. Hate speech detection has been web portals in Korea decided to close the comment actively done for languages such as English, German, or Italian, where manually labeled system in their entertainment news aggregating ser- corpus has been released. In this work, we vice (Yeo, 2019; Yim, 2020). Even though the toxic first present 9.4K manually labeled entertain- comments are now avoidable in those platforms, ment news comments for identifying Korean the fundamental problem has not been solved yet. toxic speech, collected from a widely used on- To cope with the social issue, we propose the line news platform in Korea. The comments first Korean corpus annotated for toxic speech de- are annotated regarding social bias and hate tection. Specifically, our dataset consists of 9.4K speech since both aspects are correlated. The inter-annotator agreement Krippendorff’s al- comments from Korean online entertainment news pha score is 0.492 and 0.496, respectively. articles. Each comment is annotated on two aspects, We provide benchmarks using CharCNN, BiL- the existence of social bias and hate speech, given STM, and BERT, where BERT achieves the that hate speech is closely related to bias (Boeck- highest score on all tasks. The models gener- mann and Turpin-Petrosino, 2002; Waseem and ally display better performance on bias iden- Hovy, 2016; Davidson et al., 2017). Considering tification, since the hate speech detection is the context of Korean entertainment news where a more subjective issue. Additionally, when BERT is trained with bias label for hate speech public figures encounter stereotypes mostly inter- detection, the prediction score increases, im- twined with gender, we weigh more on the preva- plying that bias and hate are intertwined. We lent bias. For hate speech, our label categorization make our dataset publicly available and open refers that of Davidson et al.(2017), namely hate, competitions with the corpus and benchmarks. offensive, and none. The main contributions of this work are as fol- 1 Introduction lows: Online anonymity provides freedom of speech to many people and lets them speak their opinions • We release the first Korean corpus manu- in public. However, anonymous speech also has a ally annotated on two major toxic attributes, 1 negative impact on society and individuals (Banks, namely bias and hate . 2010). With anonymity safeguards, individuals eas- • We hold Kaggle competitions234 and provide ily express hatred against others based on their benchmarks to boost further research develop- superficial characteristics such as gender, sexual ment. orientation, and age (ElSherief et al., 2018). Some- • We observe that in our study, hate speech de- times the hostility leaks to the well-known people tection benefits the additional bias context. who are considered to be the representatives of targeted attributes. 1https://github.com/kocohub/korean-hate-speech 2www.kaggle.com/c/korean-gender-bias-detection *Both authors contributed equally to this manuscript. 3www.kaggle.com/c/korean-bias-detection yThis work was done after the graduation. 4www.kaggle.com/c/korean-hate-speech-detection 25 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 25–31 Vancouver, Canada, July 30 - August 4, 2017. c 2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17 2 Related Work The construction of hate speech corpus has been explored for a limited number of languages, such as English (Waseem and Hovy, 2016; Davidson et al., 2017; Zampieri et al., 2019; Basile et al., 2019), Figure 1: A sample comment from the online news plat- Spanish (Basile et al., 2019), Polish (Ptaszynski form. It is composed of six parts: written date and time, et al., 2019), Portuguese (Fortuna et al., 2019), and masked user id, content, the number of replies, and Italian (Sanguinetti et al., 2018). the number of up/down votes (from top left to bottom For Korean, works on abusive language have right). mainly focused on the qualitative discussion of the terminology (Hong, 2016), whereas reliable We prepared other 2M comments by gathering and manual annotation of the corpus has not yet the top 100 sorted with the same score for all arti- been undertaken. Though profanity termbases are cles and removed with any overlaps regarding the currently available56, term matching approach fre- above 10K comments. This additional corpus is quently makes false predictions (e.g., neologism, distributed without labels, expected to be useful polysemy, use-mention distinction), and more im- for pre-training language models on Korean online portantly, not all hate speech are detectable using text. such terms (Zhang et al., 2018). In addition, hate speech is situated within the 4 Annotation context of social bias (Boeckmann and Turpin- The annotation was performed by 32 annotators Petrosino, 2002). Waseem and Hovy(2016) and consisting of 29 workers from a crowdsourcing Davidson et al.(2017) attended to bias in terms of platform DeepNatural AI7 and three natural lan- hate speech, however, their interest was mainly in guage processing (NLP) researchers. Every com- texts that explicitly exhibit sexist or racist terms. In ment was provided to three random annotators to this paper, we consider both explicit and implicit assign the majority decision. Annotators are asked stereotypes, and scrutinize how these are related to to answer two three-choice questions for each com- hate speech. ment: 3 Collection 1. What kind of bias does the comment contain? We constructed the Korean hate speech corpus us- • Gender bias, Other biases, or None ing the comments from a popular domestic enter- 2. Which is the adequate category for the com- tainment news aggregation platform. Users had ment in terms of hate speech? been able to leave comments on each article be- • Hate, Offensive, or None fore the recent overhaul (Yim, 2020), and we had scrapped the comments from the most-viewed arti- They are allowed to skip comments which are cles. too ambiguous to decide. Detailed instructions are In total, we retrieved 10,403,368 comments from described in AppendixA. Note that this is the first 23,700 articles published from January 1, 2018 to guideline of social bias and hate speech on Korean February 29, 2020. We draw 1,580 articles using online comments. stratified sampling and extract the top 20 comments 4.1 Social Bias ranked in the order of Wilson score (Wilson, 1927) Since hate speech is situated within the context on the downvote for each article. Then, we remove of social bias (Boeckmann and Turpin-Petrosino, duplicate comments, single token comments (to 2002), we first identify the bias implicated in the eliminate ambiguous ones), and comments com- comment. Social bias is defined as a preconceived posed with more than 100 characters (that could evaluation or prejudice towards a person/group convey various opinions). Finally, 10K comments with certain social characteristics: gender, politi- are randomly selected among the rest for annota- cal affiliation, religion, beauty, age, disability, race, tion. or others. Although our main interest is on gen- 5 https://github.com/doublems/korean-bad-words der bias, other issues are not to be underestimated. 6https://github.com/LDNOOBW/List-of-Dirty-Naughty- Obscene-and-Otherwise-Bad-Words 7https://app.deepnatural.ai/ 26 Thus, we separate bias labels into three: whether (%) Hate Offensive None Sum (Bias) the given text contains gender-related bias, other Gender 10.15 4.58 0.98 15.71 biases, or none of them. Additionally, we introduce Others 7.48 8.94 1.74 18.16 a binary version of the corpus, which counts only None 7.48 19.13 39.08 65.70 the gender bias, that is prevalent among the enter- Sum (Hate) 25.11 32.66 41.80 100.00 tainment news comments. The inter-annotator agreement (IAA) of the label Table 1: Distribution of the annotated corpus. is calculated based on Krippendorff’s alpha (Krip- pendorff, 2011) that takes into account an arbitrary • If a comment conveys sarcasm via rhetorical number of annotators labeling any number of in- expression or irony stances. IAA for the ternary classes is 0.492, which • If a comment states an opinion in an unethical, means that the agreement is moderate. For the bi- rude, coarse, or uncivilized manner nary case, we obtained 0.767, which implies that • If a comment implicitly attacks individ- the identification of gender and sexuality-related ual/group while leaving rooms to be consid- bias reaches quite a substantial agreement. ered as freedom of speech 4.2 Hate Speech The instances that do not meet the boundaries Hate speech is difficult to be identified, espe- above were categorized as none. The IAA on the cially for the comments which are context-sensitive. hate categories is α = 0.496, which implies a mod- Since annotators are not given additional infor- erate agreement. mation, labeling would be diversified due to the difference in pragmatic intuition and background 5 Corpus knowledge thereof. To collect reliable hate speech Release From the 10k manually annotated cor- annotation, we attempt to establish a precise and pus, we discard 659 instances that are either clear guideline. skipped or failed to reach an agreement. We split We consider three categories for hate speech: the final dataset into the train (7,896), validation hate, offensive but not hate, and none.
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