• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2021년도 제33회 한글 및 한국어 정보처리 학술대회
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• pp.85-90
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• 2021

코로나바이러스감염증-19로 인한 팬데믹 상황이 지속되면서 감염증 정보의 불확실성으로 인해 코로나 관련 루머가 온라인상에서 빠르게 전파되고 있다. 이러한 코로나 관련 가짜 뉴스를 사전에 탐지하기 위해, 본 연구에서는 한국어 코로나 가짜 뉴스 데이터셋을 구축하고, 개체명과 사용자 재확산 정보를 이용한 한국어 가짜 뉴스 탐지 모델을 제안한다. 가짜 뉴스 팩트체킹 언론인 서울대팩트체크센터에서 코로나 관련 루머 및 가짜 뉴스에 대한 검증 기사를 수집한 후, 기사로부터 개체명 추출 모델을 통해 주제 키워드를 추출하고, 이를 이용해 유튜브 상의 사용자 재확산 정보를 수집하여 데이터셋을 구성하였다. BERT 기반의 제안 모델을 다양한 비교군과 비교하였고, 특성 조합에 따른 실험을 통해 각 특성 정보(기사 텍스트, 개체명 데이터, 유튜브 데이터)가 가짜 뉴스 탐지 성능에 미치는 영향을 알아보았다.

• Journal of Information Technology Applications and Management
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• 제28권5호
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• pp.41-50
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• 2021

As COVID-19 spread widely, and rapidly, the number of misinformation is also increasing, which WHO has referred to this phenomenon as "Infodemic". The purpose of this research is to develop detection and rectification of COVID-19 misinformation based on Open-domain QA system and text similarity. 9 testing conditions were used in this model. For open-domain QA system, 6 conditions were applied using three different types of dataset types, scientific, social media, and news, both datasets, and two different methods of choosing the answer, choosing the top answer generated from the QA system and voting from the top three answers generated from QA system. The other 3 conditions were the Closed-Domain QA system with different dataset types. The best results from the testing model were 76% using all datasets with voting from the top 3 answers outperforming by 16% from the closed-domain model.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.82-91
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• 2023

In 2020 when the COVID-19 pandemic began in full-scale, the WHO Director-General warned of the dangers of an infodemic. The infodemic is a phenomenon in which false information spreads rapidly like an epidemic and causes chaos, and it was noted that the COVID-19 pandemic is not just limited to health problems, but also linked to a variety of issues such as human rights, economic inequality, various discrimination, hate speech, fake news, global governance etc. In the field of education, it is necessary to think about how to connect this global situation with school classes. Accordingly, this study suggested the direction for global citizenship education by analyzing how the infodemic spreads on Korean social media with the case of the recent global COVID-19 pandemic. According to the research results, the rate of negative emotions was higher than positive ones in the emotions that generate infodemic, while anxiety and anger were focused among negative emotions. In addition, the infodemic tended to spread widely with the feelings of anger rather than anxiety, and the feelings of anger led to advocating aggressive policies against certain country and regions. Therefore, global citizenship education is required to focus on a sense of duty and responsibility as a citizen, not on the level of national identity based on an exclusive sense of belonging. Furthermore, global citizenship education needs to lead to presenting a blueprint for education in a way that can enhance the awareness of the global community for joint response to global challenges and realize common prosperity based on sustainability and justice.

• 한국융합학회논문지
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• 제12권1호
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• pp.1-9
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• 2021

최근 우리나라는 IT기술 발전 등의 환경 변화에 따라 새로운 유형의 보안 범죄가 꾸준히 발생하고 있으며, 이러한 위협에 대한 대응은 개인이나 기업뿐만 아니라 안전한 사회의 구축을 위해 국가 차원에서 수행되어야 할 핵심과제가 되었다. 한편 코로나19 팬데믹 이후 비대면 시대가 도래하면서 기존 IT발전에 따른 보안 위협과 비대면 시대의 특징이 결합된 신종 융합보안 위협이 우리 사회를 위협하고 있다. 이에 이러한 새로운 차원의 위협을 예방하고 교정하기 위한 연구가 지속 요구되고 있으며, 이를 위해 본 연구에서는 1장에서 신 융합보안 위협이 발생한 원인과 관련 선행연구를 살펴보고 2장에서는 비대면 시대에 주요 융합보안 위협 5가지로 사이버보안·가짜뉴스·원격투표·원격근무 및 영상보안 위협을 선정하여 유형별 특징에 대해 설명하였으며. 3장에서는 이에 대한 대응 방안의 논의와 정책적 함의를 고찰하였고, 4장에서는 결론과 함께 향후 연구방향을 제시하였다.

• Asian Journal for Public Opinion Research
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• 제12권1호
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• pp.53-74
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• 2024

The proliferation of health misinformation gained momentum amidst the outbreak of the novel coronavirus disease 2019 (COVID-19). People stuck in their homes, without work pressure, regardless of health concerns towards personal, family, or peer groups, consistently demanded information. People became engaged with misinformation while attempting to find health information content. This study used the content analysis method and analyzed 1,154 misinformation stories from four prominent signatories of the International Fact-Checking Network during the pandemic. The study finds the five main categories of misinformation related to the COVID-19 pandemic. These are 1) the severity of the virus, 2) cure, prevention, and treatment, 3) myths and rumors about vaccines, 4) health authorities' guidelines, and 5) personal and social impacts. Various sub-categories supported the content characteristics of these categories. The study also analyzed the emotional valence of health misinformation. It was found that misinformation containing negative sentiments got higher engagement during the pandemic. Positive and neutral sentiment misinformation has less reach. Surprise, fear, and anger/aggressive emotions highly affected people during the pandemic; in general, people and social media users warning people to safeguard themselves from COVID-19 and creating a confusing state were found as the primary motivation behind the propagation of misinformation. The present study offers valuable perspectives on the mechanisms underlying the spread of health-related misinformation amidst the COVID-19 outbreak. It highlights the significance of discerning the accuracy of information and the feelings it conveys in minimizing the adverse effects on the well-being of public health.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.1022-1034
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• 2023

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times. To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas. In this study, we identified and predicted emerging convergences with the help of AI-associated research abstracts collected from the SCOPUS database. The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI. The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications. Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences. Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing. These findings are expected to have policy implications for facilitating the convergences in diverse industries. Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective.

• Journal of Information Science Theory and Practice
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• 제8권3호
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• pp.77-90
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• 2020

This study analyzes N=181 social media rumors from Bangladesh to find out the most popular themes, sources, and aims. The result shows that social media rumors have seven popular themes: political, health & education, crime & human rights, religious, religiopolitical, entertainment, and other. Also, online media and mainstream media are the two main sources of social media rumors, along with three tentative aims: positive, negative, and unknown. A few major findings of this research are: Political rumors dominate social media, but its percentage is decreasing, while religion-related rumors are increasing; most of the social media rumors are negative and emerge from online media, and social media itself is the dominant online source of social media rumors; and, most of the health-related rumors are negative and surge during a crisis period, such as the COVID-19 pandemic. This paper identifies some of its limitations with the data collection period, data source, and data analysis. Providing a few research directions, this study also elucidates the contributions of its results in academia and policymaking.

• Asia pacific journal of information systems
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• 제34권1호
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• pp.91-113
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• 2024

In the wake of the COVID-19 pandemic, unsubstantiated information concerning vaccines and the coronavirus has proliferated on various social media platforms. Consequently, we have considered viable actions to mitigate the impact of such unverified content, enabling individuals to use social media platforms more effectively and minimize any ensuing confusion. Recent measures in this area have included YouTube's practice of labeling vaccine or corona videos as authoritative when emanating from reputable organizations and Twitter's practice of flagging vaccine-related content as potentially misleading or taken out of context. This study seeks to explore how such contrasting labeling practices influence users' believability and engagement differentially, while also examining the moderating impact of regulatory focus. The results indicate that authoritative labeling positively influenced users' believability and engagement, whereas misleading labeling adversely affected users' believability and engagement. Additionally, our findings revealed that authoritative labeling has a stronger impact on promotion-focused individuals, while misleading labeling has a more pronounced effect on prevention-focused individuals. Our findings offer insights into how social media platforms can design and present information to their users, taking into account their regulatory focus.