Tae‐Young Kang

TaeYoung Kang, Eunrang Kwon, Junbum Lee et al.

We suggest a multilabel Korean online hate speech dataset that covers seven categories of hate speech: (1) Race and Nationality, (2) Religion, (3) Regionalism, (4) Ageism, (5) Misogyny, (6) Sexual Minorities, and (7) Male. Our 35K dataset consists of 24K online comments with Krippendorff's Alpha label accordance of .713, 2.2K neutral sentences from Wikipedia, 1.7K additionally labeled sentences generated by the Human-in-the-Loop procedure and rule-generated 7.1K neutral sentences. The base model with 24K initial dataset achieved the accuracy of LRAP .892, but improved to .919 after being combined with 11K additional data. Unlike the conventional binary hate and non-hate dichotomy approach, we designed a dataset considering both the cultural and linguistic context to overcome the limitations of western culture-based English texts. Thus, this paper is not only limited to presenting a local hate speech dataset but extends as a manual for building a more generalized hate speech dataset with diverse cultural backgrounds based on social science perspectives.

TaeYoung Kang

Based on the 10.9K articles from top 40 news providers of South Korea, this paper analyzed the media framing of Itaewon Halloween Crowd Crush during the first 72 hours after the incident. By adopting word-vector embedding and clustering, we figured out that conservative media focused on political parties' responses and the suspect's identity while the liberal media covered the responsibility of the government and possible unequal spillover effect on the low-income industry workers. Although the social tragedy was not directly connected to institutional politics, the media clearly exhibited political bias in the coverage process.