“Why do I feel offended?” - K orean Dataset for Offensive Language Identification (2023)

Abstract: Warning: This paper contains some offensive expressions. Offensive content is an unavoidable issue on social media. Most existing offensive language identification methods rely on the compilation of labeled datasets. However, existing methods rarely consider low-resource languages that have relatively less data available for training (e.g., Korean). To address these issues, we construct a novel KOrean Dataset for Offensive Language Identification (KODOLI). KODOLI comprises more fine-grained offensiveness categories (i.e., not offensive, likely offensive, and offensive) than existing ones. A likely offensive language refers to texts with implicit offensiveness or abusive language without offensive intentions. In addition, we propose two auxiliary tasks to help identify offensive languages: abusive language detection and sentiment analysis. We provide experimental results for baselines on KODOLI and observe that language models suffer from identifying “LIKELY” offensive statements. Quantitative results and qualitative analysis demonstrate that jointly learning offensive language, abusive language and sentiment information improves the performance of offensive language identification.

## introduction data-driven approaches for detecting and measuring offensive content have steadily grown from statistical methodologies to deep learning models for natural language processing @xcite . although various methods for detecting offensive language have been proposed, most of them rely on composing training datasets to determine whether a statement is offensive @xcite @xcite . in south korea, most of the population actively uses the internet, and the size of online communities is large compared with the population @xcite . the social problems caused by offensive comments have also increased @xcite . therefore, we need to analyze and discuss korean texts and their offensiveness. recent approaches have been studied to understand offensive language based on the typology of @xcite , which differentiates whether the abusive language is directed to a specific individual or group, and whether it is explicit or implicit @xcite . this typology helps to identify the offensive language from the statement. however, most existing studies @xcite have considered the offens-ive language detection problem as a binary classification task for distinguishing offensive languages. although a few studies distinguish profanity and insults under offense @xcite , they are limited in classifying various types of offensive language. for instance, offensive intention can be hidden under rhetorical expressions or abusive language can be used without offensive intentions. in particular, in online communities, users freely express their opinions without self-censorship. for instance, users often emphasize emotions with profanity without any offensive intention, as shown in figure 1 @xcite . in addition, comments on news media (i.e., strictly regulated platforms) are sophisticated in their expressions (i.e., sarcasm or twists) to avoid blocking, as shown in figure 1(b) 1 . to address these issues, we propose a novel offensive language identification (oli) task that has three classes: not offensive, likely offensive, and offensive (we extend the existing oli task by adding a likely offensive class). moreover, we analyze the attributes of the offensive language. offensiveness is closely associated with abuse @xcite . several studies @xcite have revealed that negative sentiment messages occur frequently in offensive languages. therefore, we propose two auxiliary tasks to effectively identify offensive languages: abusive language detection (ald) and sentiment analysis (sa). the ald task aims to detect literally abusive language, whereas the sa task extracts the speaker's subjectivity beyond the sentence. a combination of tasks can be useful for detecting various offensive cases and interpreting the attributes of offensiveness. we use kodoli to build classifiers using pretrained language models (plms) @xcite and feature-based models @xcite . we observe that these models struggle to identify likely offensive comments. we utilize a multi-task learning (mtl) technique to utilize related tasks (i.e., ald and sa). in a qualitative analysis, models that integrate information from offensive language, abusive language, and sentiment exhibit consistent and bettercontextualized predictions than those that use only offensive language information. the contributions of this study are as follows: offensive language datasets offensive language is correlated with several other linguistic and social phenomena including abusive and aggressive language, cyberbullying, racism, extremism, radicalization, toxicity, profanity, and hate speech @xcite . as hate speeches increased, the number of corpora annotating offensive languages increased @xcite @xcite . a previous study @xcite proposed a novel dataset that provides a scheme for classifying the type and target as well as offensive language. other studies @xcite @xcite have been categorized into explicit and implicit offensive instances. however, none of the aforementioned studies handles the korean offensive language. to the best of our knowledge, the present study is one of only a few studies that address the korean offensive language by introducing related auxiliary tasks. most recently, the concurrent study @xcite has proposed korean offensive language dataset that includes target group, offensive span, and target span annotations as well as offensiveness annotation. they focus on justifying the decision for offensiveness through auxiliary tasks (i.e., target of insult, offensive span). in this study, we focus on subdividing the degree of offensiveness by adding the likely offensive category and auxiliary tasks (i.e., ald and sa). abusive language detection abuse encompasses many types of fine-grained negative expressions. for instance, nobata et al. used the term 'abuse' to refer collectively to hate speech, derogatory language, and profanity, whereas mishra et al. considered racism and sexism as abuse. we follow the definition of abusive language suggested by park et al.: (i) profanity is a word or phrase that insults or curses others; (ii) hate speech is an act of hostile expression based on negative prejudice against a group that has been historically discriminated against because of race, ethnicity, religion, gender, sexual orientation, and gender identity (cho and @xcite . sentiment analysis sa identifies and measures opinions, specifically in determining whether a writer's attitude toward a particular topic is positive, negative, or neutral @xcite @xcite . recent studies have investigated the benefits of using sentiment features in oli. for instance, rodriguez et al. applied sa to detect posts suspected of instigating hatred containing highly negative tones. in addition, plaza-del arco et al. demonstrated that polarity knowledge can be useful for detecting hate speech and offensive languages more accurately across datasets in spanish tweets. inspired by the prior studies, we propose kodoli, which contains ald and sa tasks as auxiliary tasks. ## task description we provide a comprehensive overview of the three tasks for framing the offensive language phenomenon as follows: (i) whether a comment is offensive, likely offensive or not, (ii) whether it contains abusive language (profanity and hate speech), and (iii) whether it has sentiment with intention. ## modeling preprocessing we randomly shuffle and split the dataset into training (26,967), validation (5,778), and testing (5,780) sets. we apply the morphemelevel pre-tokenization, which is effective for character-rich languages @xcite . specifically, we select mecab-ko foot_7 @xcite , a pre-tokenizer adapted for koreans. in the case of bert-family models, we apply the wordpiece tokenizer following the work @xcite . multi-task learning mtl has been widely used to train with data from multiple tasks, and we use the hard parameter sharing technique @xcite . this is the practice of sharing model weights between related tasks; therefore, each weight is trained to minimize multiple loss func- ## experimental results we first experiment with the single-task learning (stl) method for the oli task using our dataset, kodoli, in the popular and powerful nlp models (the implementation details are described in appendix a.4). further, we experiment with the mtl method by combining the oli task with auxiliary task 1 (ald) or auxiliary task 2 (sa), which are our proposed approaches. we evaluate the experimental performance using the following metrics: precision (p), recall (r), f1-score (f1) for each class and macro-averaging scores. ## conclusion in this paper, we introduced kodoli, a new korean dataset for oli. to this end, we collected various offensive comments from online communities and news articles in diverse domains. in particular, we expanded a fine-grained label called 'likely offensive' to distinguish the implicitly offensive and abusive comments with no targeted offense. we proposed two auxiliary tasks to help models identify offensive languages: ald and sa. finally, we released 38k comments annotated with offensive language, abusive language, and sentiment information. using kodoli, we demonstrated that modeling offensive language using abusive language and sentiment was effective in quantitative and qualitative analyses. we expect our research will benefit further studies that analyze offensiveness in korean.