• Korean journal of communication and information
• /
• v.79
• /
• pp.165-195
• /
• 2016

This paper aims to examine the third-person effect(TPE) of hate comments on online news, and analyze how the issue-relevant audience factors as well as the characteristics of the online message have influence on the TPE. More specifically, based on the distinction between hateful and logical comments regarding the issue of illegal immigration, the authors have conducted an online experiment that compares how the message-related features, i.e., ways of expressing the ideas, lead to the difference in TPE. Analysis was also conducted with regards to how political orientation and discriminatory predisposition to immigrants among the audiences, have different impacts on the TPE. The 479 participants in the experiments were randomly assigned to experimental group(exposed to hate comments) or control group(exposed to logical comments). The results reveal that the TPE of hate comments is higher than that of logical message. The same message proved to be more effective for news users with liberal orientation and discriminatory predisposition. The significance of this paper lies in that it has examined the effect of online hate comments in a rigorous experimental setting. Also the research further elaborated on the audience-related variables, for which the previous studies tended to focus those on the general psychological level rather than relate them more specifically to the issues.

• Journal of Information Processing Systems
• /
• v.19 no.5
• /
• pp.641-651
• /
• 2023

Malicious hate speech and gender bias comments are common in online communities, causing social problems in our society. Gender bias and hate speech detection has been investigated. However, it is difficult because there are diverse ways to express them in words. To solve this problem, we attempted to detect malicious comments in a Korean hate speech dataset constructed in 2020. We explored bidirectional encoder representations from transformers (BERT)-based deep learning models utilizing hyperparameter tuning, data sampling, and logits ensembles with a label distribution. We evaluated our model in Kaggle competitions for gender bias, general bias, and hate speech detection. For gender bias detection, an F1-score of 0.7711 was achieved using an ensemble of the Soongsil-BERT and KcELECTRA models. The general bias task included the gender bias task, and the ensemble model achieved the best F1-score of 0.7166.

• The Journal of the Korea Contents Association
• /
• v.20 no.2
• /
• pp.499-510
• /
• 2020

This study aims to analyze the causes of Internet hate speech, which has recently been emerging as a serious social problem and to seek for countermeasures. The experiences of hate speech are examined through the analysis of college students' essays and the causes and solutions of hate speech are suggested through the in-depth interviews with the experts. College students experience hate speech on the Internet on the basis of attributes such as age, gender, sexual orientation, and regionalism. Online comments on news, social media and online games are the main sources in spreading hate speech. On a personal level the lack of awareness of human dignity and the absence of media education are diagnosed as the reasons for online hate speech. The social reasons for online hate speech lie in the lack of human rights education and the problems of the media. In order to improve the problems of Internet hate speech, various suggestions are proposed on the legal, social and educational levels.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.12
• /
• pp.1595-1603
• /
• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• Asian Journal for Public Opinion Research
• /
• v.7 no.3
• /
• pp.169-198
• /
• 2019

This study focused on the increase in refugee-related discourse in Korean society with the recent inflow of asylum seekers to Jeju Island. The purpose of our study was to understand the trends in public opinion concerning the acceptance of refugees by analyzing the content of refugee-related video commentary on YouTube. Topic modeling was conducted to analyze the main points, context, and ideas in the comments. The results indicated that the media mainly focus on the pros and cons of refugees, restricting the refugee issue to the problem of acceptance with a narrow focus on the case of Jeju Island. Refugee acceptance was treated as overwhelmingly unacceptable in the comments. We found that commenters often used negative discourse in the comments as a device for reproducing and amplifying hate speech.

• 한국노년학
• /
• v.41 no.4
• /
• pp.505-525
• /
• 2021

Purpose. The purpose of this study is to examine how the phrase "old person" are expressed and used in the online sphere. Based on the theoretical concept of stigma, this study investigates the images and attitudes in society toward the elderly, and the characteristics of hate speech aimed at the elderly. Method. This study conducted text mining based on social big data using anonymous conversations. Results. It was confirmed that the elderly images shared online were generally negative. The attitudes expressed toward them also tended to be negative due to the negative images that are propagated of the elderly. The hate speech relating to the elderly, in usages such as 'Teul-ttag' and 'Kon-dae', were mainly identified in comments that negatively evaluate the elderly, and these expressions demonstrate the depth of hate and discrimination towards the elderly who are considered burdensome by young people. Interestingly, the hateful expressions towards the elderly were found more with regard to issues related to politics and economics and not just any content about the elderly. Conclusions. This study discussed the ways and means to enhance inter-generational understanding and solidity.

• Annual Conference on Human and Language Technology
• /
• 2020.10a
• /
• pp.298-303
• /
• 2020

온라인 공간에서 특정인, 혹은 특정 집단의 사람들을 대상으로 한 혐오 표현은 당사자에게 정신적 고통을 미칠 뿐 아니라 이를 보는 이에게도 간접적인 불쾌함을 유발한다. 이에 관한 문제의식은 사회적으로 공감대가 형성된 바 있지만, 아직 한국어에서는 많은 연구들이 혐오 표현 자체의 논의에 집중하고 있으며, 이는 실제로 관찰되는 혐오 표현들의 자동 탐지 및 예방에는 효과적인 정보를 제공하지 못하는 것이 사실이다. 이에 우리는 실제 온라인 댓글들을 탐구하여 혐오, 모욕 및 사회적 편견을 탐지할 수 있는 모델 학습에 필요한 코퍼스 구축 가이드라인을 제작하였다. 구체적인 사례를 동반한 가이드라인과 크라우드소싱을 바탕으로 약 9천 3백 문장 가량의 코퍼스를 구축하였으며, 해당 데이터에 관한 개요와 함께 우리의 접근 방식이 어떤 점에서 기존의 담론과 연관되어 있는지에 대한 분석을 제시한다.

• Smart Media Journal
• /
• v.10 no.4
• /
• pp.71-79
• /
• 2021

One of the social aspects that changes as the use of the Internet becomes widespread is communication in online space. In the past, only one-on-one conversations were possible remotely, except when they were physically in the same space, but nowadays, technology has been developed to enable communication with a large number of people remotely through bulletin boards, communities, and social network services. Due to the development of such information and communication networks, life becomes more convenient, and at the same time, the damage caused by rapid information exchange is also constantly increasing. Recently, cyber crimes such as sending sexual messages or personal attacks to certain people with recognition on the Internet, such as not only entertainers but also influencers, have occurred, and some of those exposed to these cybercrime have committed suicide. In this paper, in order to reduce the damage caused by malicious comments, research a method for improving the performance of discriminate malicious comments through feature extraction based on parts-of-speech.

• Korean journal of communication and information
• /
• v.75
• /
• pp.43-79
• /
• 2016

An increase in the number of immigrants to Korea since the late 1980s' has signified the proliferation of globalization and global capitalism. In Korea, most married immigrants are women, as the culture emphasizes patrilineage and the stability of the institution of marriage, particularly in rural areas. Immigrant women have experienced dual ordeals. The Aogra Internet community in Korea has been one of the most representative sites that has shown the power of communities in cyberspace since 2002, leading the discussion of social issues and deliberative democracy both online and offline. This paper analyzed Koreans' writings (such as long comments) on immigrant women in the Agora community. The analysis revealed the following results: first, immigrant women were referred to using terms related to prostitution, with excessive expression of disgust, which is called a "narrative of identity." Second, anti-multiculturalists called Korean men victims of married immigrant women and expressed hatred toward immigrant women, which is called a "narrative of sacrifice." Third, anti-multiculturalists justified their emotions as just resentment based on ideas of justice, equality, and patriotism, concealing the emotion of disgust, which is called the "narrative of justice, equality." Fourth, antimulticulturalists played roles to spread the emotion of disgust, by repeatedly referring to international marriage fraud and immigrant workers' crimes, which is called "narrative of crime." Fifth, some positive writings on immigrant women were based on empathy(a concept defined in this context by Martha Nussbaum), but they can be analyzed as narratives encouraging cultural integration through the perspective of orientalism. Therefore, comments on immigrant women in the Agora represent a "catch-22" dilemma. To deal with conflicts arising from disgust and violations of human rights, civic education focusing on humanism is needed in this multicultural era.