Korean Online Hate Speech Dataset for Multilabel Classification: How Can Social Science Improve Dataset on Hate Speech?

Kang, Tae‐Young; Kwon, Eunrang; Lee, Junbum; Nam, Youngeun; Song, Jung‐Hun; Suh, JeongKyu

doi:10.48550/arxiv.2204.03262

Cited by 4 publications

(4 citation statements)

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“…This was quite common when the given keywords were used in traditional contexts or as hateful slang. To distinguish between the two, we employed Kang et al [ 32 ]’s deep learning-based hate detection model. In doing so, we set a threshold of 0.9 to minimize unnecessary data loss.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the rapid digital transition has made older and elderly adults vulnerable to stigmatization for not adopting quickly, and in response, derogatory and defamatory expressions targeting the younger generation are commonplace online [ 32 ]. Consequently, we aimed to assess the degrees of hatred targeting older versus younger people in online communities with searches for the following three keywords: (a) teulttak , insulting old people by calling them dentures, (b) jaemmin , a clueless, ignorant child, and (c) yuchung , larva, a term used for humiliating a child.…”

Section: South Korean Hatred In Actionmentioning

confidence: 99%

“…An ontological dictionary is needed to extract keywords from a given text, and therefore, we initially collected all keywords that appeared in previous studies on online hate in South Korea [32,[44][45][46][47][48][49]. We monitored online communities for about three months to determine if any terms were missing from our initial list of keywords.…”

Section: Data Collectionmentioning

confidence: 99%

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The hatred of all against all? Evidence from online community platforms in South Korea

Koo,

Suh,

Chung

et al. 2024

PLoS ONE

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Background Over several years of recent efforts to make sense and detect online hate speech, we still know relatively little about how hateful expressions enter online platforms and whether there are patterns and features characterizing the corpus of hateful speech. Objective In this research, we introduce a new conceptual framework suitable for better capturing the overall scope and dynamics of the current forms of online hateful speech. Methods We adopt several Python-based crawlers to collect a comprehensive data set covering a variety of subjects from a multiplicity of online communities in South Korea. We apply the notions of marginalization and polarization in identifying patterns and dynamics of online hateful speech. Results Our analyses suggest that polarization driven by political orientation and age difference predominates in the hateful speech in most communities, while marginalization of social minority groups is also salient in other communities. Furthermore, we identify a temporal shift in the trends of online hate from gender to age based, reflecting the changing sociopolitical conditions within the polarization dynamics in South Korea. Conclusion By expanding our understanding of how hatred shifts and evolves in online communities, our study provides theoretical and practical implications for both researchers and policy-makers.

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Section: Methodsmentioning

confidence: 99%

Section: South Korean Hatred In Actionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

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The hatred of all against all? Evidence from online community platforms in South Korea

Koo,

Suh,

Chung

et al. 2024

PLoS ONE

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“…Rizwan et al, 2020) remain scarce, limiting the development of hate speech detection in other languages. Although there are existing publicly available Korean hate speech corpora (Moon et al, 2020;Kang et al, 2022), our dataset is bigger in size, focuses on more diverse aspects of hate speech and includes span-based annotation.…”

Section: Introductionmentioning

confidence: 99%

KOLD: Korean Offensive Language Dataset

Jeong¹,

Oh²,

Ahn³

et al. 2022

Preprint

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Warning: this paper contains content that may be offensive or upsettingAlthough large attention has been paid to the detection of hate speech, most work has been done in English, failing to make it applicable to other languages. To fill this gap, we present a Korean offensive language dataset (KOLD), 40k comments labeled with offensiveness, target, and targeted group information. We also collect two types of span, offensive and target span that justifies the decision of the categorization within the text. Comparing the distribution of targeted groups with the existing English dataset, we point out the necessity of a hate speech dataset fitted to the language that best reflects the culture. Trained with our dataset, we report the baseline performance of the models built on top of large pretrained language models. We also show that title information serves as context and is helpful to discern the target of hatred, especially when they are omitted in the comment.

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