K- HATERS : A Hate Speech Detection Corpus in K orean with Target-Specific Ratings (2023)

Abstract: Numerous datasets have been proposed to combat the spread of online hate. Despite these efforts, a majority of these resources are English-centric, primarily focusing on overt forms of hate. This research gap calls for developing high-quality corpora in diverse languages that also encapsulate more subtle hate expressions. This study introduces K-HATERS, a new corpus for hate speech detection in Korean, comprising approximately 192K news comments with target-specific offensiveness ratings. This resource is the largest offensive language corpus in Korean and is the first to offer target-specific ratings on a three-point Likert scale, enabling the detection of hate expressions in Korean across varying degrees of offensiveness. We conduct experiments showing the effectiveness of the proposed corpus, including a comparison with existing datasets. Additionally, to address potential noise and bias in human annotations, we explore a novel idea of adopting the Cognitive Reflection Test, which is widely used in social science for assessing an individual’s cognitive ability, as a proxy of labeling quality. Findings indicate that annotations from individuals with the lowest test scores tend to yield detection models that make biased predictions toward specific target groups and are less accurate. This study contributes to the NLP research on hate speech detection and resource construction. The code and dataset can be accessed at https://github.com/ssu-humane/K-HATERS.

## introduction hate speech is defined by "offensive discourse targeting a group or an individual based on inherent characteristics (such as race, religion or gender)" 1 . this is a critical problem in society that can aggravate polarization and threaten democracy @xcite . the massive volume of communication in the web and social media amplifies 1 https://www.un.org/en/hate-speech/understanding-hatespeech/what-is-hate-speech the negative impacts of hate speech. to curb its spread in online environments, previous nlp research has developed ml-based detection models. labeled resources play a critical role in building supervised detection models; thus, a various form of offensive language and hate speech datasets have been proposed in the nlp community, and they facilitated the development of detection methods @xcite . despite the advancement in the online hate speech research, there are several rooms for improvement. (1) language: most of the existing datasets are limited to english and high-resourced languages @xcite . it is critical to construct a non-english dataset for the development of detection models that are aware of culture-specific expressions. (2) implicitness: the majority of previous research conducted on this subject has primarily concentrated on explicit manifestations of offensive language. a few studies investigated the implicit form of hateful content @xcite @xcite , most of which targeted english. (3) biased and noisy labels: manual annotation is a predominant approach for constructing labeled data. however, the labeling process could reflect inherent biases of human annotators, and it is hard to control the label quality @xcite . training a model on such a noisy and biased dataset can amplify biases by inference @xcite . this paper aims to fill the three research gaps by presenting a new hate speech detection corpus in korean with target-specific ratings, named k-haters. (1) among korean offensive language corpora, this resource represents the most extensive data collection to date, comprising 192,158 news comments and surpassing the size of the previously largest corpus by 1.75 times @xcite . (2) we design a rating scheme for target-specific and fine-grained offensiveness on a three-point likert scale. it distinguishes between the explicit form of offensiveness (e.g., toxic expression) and the implicit form (e.g., sarcasm, stereotypes), facilitating a nuanced understanding of online hate. the key findings are summarized as follows: ## related works early studies used the binary label for classifying hate and normal text @xcite . @xcite proposed using a taxonomy capturing a middle ground between hate speech and normal text. an abusive comment that is not hate speech is generally called offensive. a later study aimed to capture targetspecific hate such as racism and sexism @xcite . most research focused on overt forms of hate speech, but explicit hate is more easily identifiable, e.g., by lexicon-based methods @xcite . recent research focused on implicitness of hate speech and proposed new datasets @xcite @xcite @xcite . a study developed a taxonomy of implicit hate and provided a labeled dataset @xcite , but its annotation scheme is not generalizable in the korean context (e.g., white grievance). in addition to prediction accuracy, recent studies focuses on human-centered desiderata of hate speech classifiers, such as explainability and fairness. @xcite proposed a dataset that includes hate speech labels and annotators' rationale as a highlighted span. a recent study developed a pretraining method of masked rationale prediction @xcite . to promote the development of fair detection models, @xcite introduced a functional test that measures a predictive bias of hate speech detection models. in the context of the korean language, a handful number of resources is available, as shown in ## cross-dataset prediction this section presents the results of a cross-dataset experiment to understand the generalizability of existing and proposed datasets, as summarized in figure 3 presents the confusion matrix, with each axis representing the label categories. the models trained on beep!, kold, and k-mhas achieved macro f1 scores of 0.528, 0.631, and 0.461, respectively. notably, the classifier trained on beep! tends to misclassify offensive-labeled classes as hate labels. in contrast, the classifier trained on k-mhas demonstrates an opposite trend: it frequently predicts normal labels for samples labeled as offensive and fails to predict hate labels for the level-1 hate samples. meanwhile, the kold model exhibited predictive accuracy at 0.629, with its performance differing by only 0.04 compared to the intra-dataset model. to gain deeper insights into the observed trends, we conducted a principal component analysis using a pretrained sentence transformer embedding 5 . figure 4 depicts the distribution of the top two principal components for samples from each class across the four datasets. the results indicate that the labels from kold and our dataset are closely aligned. in contrast, k-mhas and beep! exhibit distinct patterns. this divergence could account for the suboptimal performance observed in crossdataset experiments. this section examines the research hypothesis that labels given by workers with a low crt score would be noisy and biased. to evaluate the hypothesis, we constructed two different training sets: we conduct a case study to understand the distribution of offensive language and hate speech in the wild. we applied the h+t model trained on the transformed unified labels to the unlabeled dataset of 191,663 news comments published in the society, world news, and politics sections shared over the period of july to august in 2021. the target dataset was randomly sampled from the initial collection, and it does not overlap with the proposed corpus. for comparative analysis, we applied the classifier to the same number of comments from the unlabeled comments posted from the entertainment section, which was published concurrently with the beep! labeled corpus @xcite . this section falls under the soft news category(lehman-wilzig and seletzky, 2010), in contrast to the hard news encompassed by our corpus. results show that offensive and hate expressions are more prevalent in the comments associated with hard news than those from soft news. among them, hate speech accounts for a larger fraction in the politics and worlds sections, targeted toward the politics and religion group as the major target (figure 6b ), respectively. by contrast, offensiveness embedded in comments from the entertainment section tend to be targeted toward individuals, such as celebrities. ## conclusion this study introduces a new corpus for hate speech detection, representing the largest offensive language corpus in korean to date. the dataset encompasses 192k news comments, each rated for target-specific offensiveness on a three-point likert scale. for efficient modeling, we introduced a label transformation strategy, aiming to unify labels for abusive language categories and multi-label target categories. experimental results highlight the effectiveness of the proposed dataset and labeling scheme. both the dataset and labeling strategy hold promise for advancing nlp research in hate speech detection. furthermore, inspired by studies in psychology and social science @xcite @xcite , we tested the hypothesis that labels annotated by workers with low crt scores-indicative of diminished attention during the labeling process-would be biased and noisy. our findings revealed that using labels provided by annotators with the lowest test scores results in detection models that are both less accurate and unfairer. this discovery supports our hypothesis and suggests the crt score's viability as a surrogate for annotation quality. to the best of our knowledge, this research is pioneering in integrating crt into the process of ml dataset construction, offering a potential avenue of contribution to the wider nlp community.