• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.499-510
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• 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 Information Processing Systems
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• v.19 no.5
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• pp.641-651
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• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.735-736
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• 2021

인터넷에서 댓글 시스템은 자신의 의사표현을 위한 시스템으로 널리 사용되고 있다. 하지만 이를 악용하여 상대방에 대한 혐오를 드러내기도 한다. 악성댓글에 대한 적절한 대처를 위해 빠르고 정확한 탐지는 필수적이다. 본 연구에서는 악성 댓글 분류 문제를 해결하기 위해서 순서가 있는 분류 레이블의 성질을 활용한 순서형 회귀 (Ordinal regression) 기반의 분류 모델을 제안한다. 일반적인 분류 모형과는 달리 혐오 발언 정도에 따라 다중 레이블을 부여하여 학습을 진행하였다. 실험을 통해 Korean Hate Speech Dataset에 대해 LSTM기반의 모형의 출력층을 다르게 구성하여 순서형 회귀 기반의 모형들의 성능을 비교하였다. 결과적으로 예측 결과에 대한 조율이 가능한 순서형 회귀 모형이 일반적인 순서형 회귀 모형에 비해서 편향된 예측에 대해 추가적인 성능 향상을 보였다.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• Journal of Contemporary Eastern Asia
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• v.19 no.2
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• pp.179-196
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• 2020

Despite the enormous success in Japan of Korean popular culture, including TV dramas and K-pop, over the past few decades, anti-Korean sentiment in the country has become increasingly visible and intense. In this article, we examine how young Japanese female fans of Korean popular culture engage with the Korean Wave discourse while also engaging with―or, rather, disengaging from―anti-Korean movements and hate speech. Whereas previous scholarship on the Korean Wave has emphasized the power of active fans' agency, this paper investigates how the fans who passionately and self-reflexively consume Korean popular culture understand and react to the growing anti-Korean sentiment in Japan. Through in-depth interviews with 15 of these fans in their 20s and 30s, we show how they have navigated the discursive space between appreciation for Korean culture and anti-Koreanism in Japan.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.331-350
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• 2022

This study aims to understand topics of incivility related to COVID-19 from analyzing Twitter posts including COVID-19-related hate speech. To achieve the goal, a total of 63,802 tweets that were created between December 1st, 2019, and August 31st, 2021, covering three targets of hate speech including region and public facilities, groups of people, and religion were analyzed. Frequency analysis, dynamic topic modeling, and keyword co-occurrence network analysis were used to explore topics and keywords. 1) Results of frequency analysis revealed that hate against regions and public facilities showed a relatively increasing trend while hate against specific groups of people and religion showed a relatively decreasing trend. 2) Results of dynamic topic modeling analysis showed keywords of each of the three targets of hate speech. Keywords of the region and public facilities included "Daegu, Gyeongbuk local hate", "interregional hate", and "public facility hate"; groups of people included "China hate", "virus spreaders", and "outdoor activity sanctions"; and religion included "Shincheonji", "Christianity", "religious infection", "refusal of quarantine", and "places visited by confirmed cases". 3) Similarly, results of keyword co-occurrence network analysis revealed keywords of three targets: region and public facilities (Corona, Daegu, confirmed cases, Shincheonji, Gyeongbuk, region); specific groups of people (Coronavirus, Wuhan pneumonia, Wuhan, China, Chinese, People, Entry, Banned); and religion (Corona, Church, Daegu, confirmed cases, infection). This study attempted to grasp the public's anti-citizenship public opinion related to COVID-19 by identifying domestic COVID-19 hate targets and keywords using social media. In particular, it is meaningful to grasp public opinion on incivility topics and hate emotions expressed on social media using data mining techniques for hate-related to COVID-19, which has not been attempted in previous studies. In addition, the results of this study suggest practical implications in that they can be based on basic data for contributing to the establishment of systems and policies for cultural communication measures in preparation for the post-COVID-19 era.

• Korean journal of applied entomology
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• v.60 no.2
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• pp.155-165
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• 2021

There are 18,638 insect species recorded in Korea, representing 35.4% of Korean Biodiversity (52,628). In this study, we classified Korean common name into 11 categories (color, size, shape, plant, location, ecology, foreign language, personal name, native, physiology, positive/negative) and assigned every species into each categories and researched the meaning of Korean common names. We conducted further analysis on 506 cases of hate speech (disability, racial·regional·appearance discriminations). Also we analyzed 508 cases of Japanese expressions (person's name, place name, Japanese language), and proposed alternative words in case of inappropriate usages.

• Journal of Contemporary Eastern Asia
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• v.18 no.2
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• pp.68-89
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• 2019

When comparing media freedom in Malaysia, Myanmar, and Thailand, so-called "fake news" appears as threats to a deliberative (online) public sphere in these three diverse contexts. However, "racist propaganda", "information operations" and "negative campaigning" might be more accurate terms that explain these forms of systematic manipulative political communication. The three cases show forms of disinformation in under-researched contexts and thereby expand the often Western focused discourses on hate speech and fake news. Additionally, the analysis shows that harmful disinformation disseminated online originates from differing contextual trajectories and is not an "online phenomenon". Drawing on an analysis of connotative context factors, this explorative comparative study enables an understanding of different forms of harmful disinformation in Malaysia, Myanmar, and Thailand. The connotative context factors were inductively inferred from 32 expert interviews providing explanations for the formation of political communication (control) mechanisms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.299-302
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• 2019

현재 게임을 즐기는 유저의 스펙트럼은 미취학 아동부터 노년에 이르기까지 매우 넓어졌다. 반면, 온라인 게임 내의 헤이트 스피치는 최근 사회적 문제로 대두되고 있다. 헤이트스피치 문제는 점차 심각해져가고 있지만 이 문제를 개선하기 위한 가이드라인은 명확히 제시되어 있지 않다. 본 논문에서는 온라인 게임 내 텍스트와 음성 채팅을 통해 발생하는 헤이트 스피치의 현황과 문제점을 알아보고 그에 대한 해결방향을 제시한다.

• 한국노년학
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• v.41 no.4
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• pp.505-525
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• 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.