• International Journal of Advanced Culture Technology
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• 제9권3호
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• pp.352-359
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• 2021

The purpose of this study is to design and build fake news discrimination systems and methods using fact-checking guidelines. In other words, the main content of this study is the system for identifying fake news using Artificial Intelligence -based Fact-checking guidelines. Specifically planned guidelines are needed to determine fake news that is prevalent these days, and the purpose of these guidelines is fact-checking. Identifying fake news immediately after seeing a huge amount of news is inefficient in handling and ineffective in handling. For this reason, we would like to design a fake news identification system using the fact-checking guidelines to create guidelines based on pattern analysis against fake news and real news data. The model will monitor the fact-checking guideline model modeled to determine the Fact-checking target within the news article and news articles shared on social networking service sites. Through this, the model is reflected in the fact-checking guideline model by analyzing news monitoring devices that select suspicious news articles based on their user responses. The core of this research model is a fake news identification device that determines the authenticity of this suspected news article. So, we propose news article identification methods with fact-checking guideline on Artificial Intelligence & Bigdata. This study will help news subscribers determine news that is unclear in its authenticity.

• Journal of Information Science Theory and Practice
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• 제11권1호
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• pp.1-13
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• 2023

This study determines how social media users (SMUs) verify the information they come across on the Internet. It determines SMUs' perception of online fact-checking services in terms of their ease of use, usefulness, and trust. By conducting a focus group discussion and key informant interviews, themes were derived in determining fact-checking practices while a survey was further conducted to determine such perceived ease of use, usefulness, and trust in fact-checking services. The thematic analysis revealed major information verification practices, such as cross-checking and verifying with other sources, inspecting comments and reactions, and confirming from personal and social networks. The results showed that SMUs considered fact-checking services easy to use. However, a concern was raised about their usefulness stemming from the delayed action in addressing the information issues that need to be verified. As to perceived trust, it was found that SMUs have reservations about fact-checking services. Finally, it is believed that fact-checking services are expected to be credible and need to be promoted to mitigate any form of fake news, particularly on social media platforms.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.19-21
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• 2021

본 논문에서는 어텐션 메커니즘에 기반하여 정보 판단에 대한 근거를 제공하는, 이른바 설명 가능한 팩트체크 모델을 제안할 것이다. 최근 미디어의 발달에 따라 각종 뉴스가 쏟아지고 있는 바, 이와 더불어 뉴스에 대한 진위 여부 판단, 즉 팩트체크가 주목받고 있는 상황이다. 하지만 현재 팩트체크는 언론인이나 시민 단체 일원들의 검색 능력에 의존하고 있어서, 이를 자동적으로 하는 모델에 대한 연구가 진행되고 있다. 이에 본 논문에서 설명 가능한 자동 팩트체크 모델을 제안하고자 한다.

• 문화기술의 융합
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• 제9권3호
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• pp.495-508
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• 2023

객관보도의 한계를 극복하고자 출발한 방송의 팩트체크 뉴스가 공정성을 담보하고 있는지를 비교하기 위해 20대 대선과 지방선거전이 치러진 2022년 1월1일부터 2022년 5월31일까지 KBS와 MBC SBS, TV조선과 JTBC MBN, YTN의 메인뉴스에 방영된 팩트체크 뉴스 227건을 비교 분석했다. 분석결과 방송사에 따라 팩트체크 검증대상과 서술방식에 뚜렷한 차이를 보였다. 대체로 MBC와 JTBC YTN이 민주당 등 진보 진영에 우호적인 서술이 많았던 반면 TV조선은 국민의 힘 등 보수 진영에 우호적인 서술이 많았다. MBN은 비교적 중립적인 서술태도를 보인 것으로 나타났다. KBS는 외견상 중립을 지키려는 흔적이 역력했다. SBS와 TV 조선이 팩트체크에 가장 적극적이었으나 이슈를 폭넓게 다루면서 사실여부를 명확하게 가리기보다는 시청자들이 궁금해 하는 이슈를 대상으로 맥락 설명에 치우친 것이 특징이다. 팩트체크 서술에서 방송사별로 이념적 편향성이 투영되는 것은 극복해야할 과제이다.

• 한국정보통신학회논문지
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• 제25권9호
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• pp.1275-1278
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• 2021

Nowadays, fake news from newspapers and social media is a serious issue in news credibility. Some of machine learning methods (such as LSTM, logistic regression, and Transformer) has been applied for fact checking. In this paper, we present Transformer-based fact checking model which improves computational efficiency. Locality Sensitive Hashing (LSH) is employed to efficiently compute attention value so that it can reduce the computation time. With LSH, model can group semantically similar words, and compute attention value within the group. The performance of proposed model is 75% for accuracy, 42.9% and 75% for Fl micro score and F1 macro score, respectively.

• 한국콘텐츠학회논문지
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• 제20권2호
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• pp.172-181
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• 2020

본 연구는 SNU팩트체크 센터(factcheck.snu.ac.kr)에 공개돼 있는 언론사의 팩트체크 기사를 분석했다. 팩트체크 기사 중 복수 언론사를 통해 검증이 이뤄진 교차검증 기사 50건을 연구 대상으로 정하고, 팩트체크 대상 주제, 유형, 성격, 언론사 간 판단 결과 일치도, 활용 근거가 무엇인지 검토했다. 연구결과, 언론사들은 대체로 대선·총선 후보자나 정치인 발언 등 정치 관련 주제를 팩트체킹하고 있었다. 팩트체크 대상의 유형은 주로 객관적인 사실이나 의견 혹은 해석이 개입될 수 있는 정보였으며, 발언 자체의 사실을 확인하는 기사나 인과관계, 비교 기준 시기나 대상에 따라 달리 해석될 수 있는 성격을 가진 정보에 대해 팩트체킹을 하고 있는 것으로 나타났다. 언론은 평균적으로 '대체로 사실 아님' 에 가까운 판단을 했으며, 주로 관계자 취재 및 관련 기관 자료, 정부 자료, 전문가 발언 등을 판단 근거로 활용하는 경향을 보였다.

• 제어로봇시스템학회논문지
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• 제21권4호
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• pp.330-335
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• 2015

Approximately 23% of traffic accidents appear to be caused by drowsiness while driving. This fact shows that drowsy driving is a big factor in many traffic accidents. Therefore, the development of a drowsiness checking system is necessary to prevent drowsy driving. In this paper, we analyse the changes of the histogram of eye region images which are acquired using a CCD camera. We develop a drowsiness checking system using this histogram change information. The experimental results show that our proposed method enhances the accuracy of checking drowsiness by nearly 98%, and can be used to prevent vehicle accidents due to the drowsiness of a driver.

• 정보관리학회지
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• 제27권4호
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• pp.109-130
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• 2010

본 연구의 목적은 기자직의 웹 행동 분석을 통해 웹 정보탐색행위 패턴의 특성을 규명하는 것이다. 이를 위해 본 연구는 기자직 23명을 대상으로 트랜잭션로그를 수집하여 분석하였다. 데이터 수집을 위하여 분석 대상의 컴퓨터에 웹 트래킹 소프트웨어를 설치하였으며, 2주 동안 총 39,860개의 웹 로그가 수집되었다. 세션의 시작과 종료 패턴, 단계별 이행 패턴, 순차규칙 모형이 분석되었으며, 기자직과 일반인의 웹 이용 패턴을 비교하였다. 이를 토대로 기자직의 웹 정보탐색행위 패턴을 사실확인형 탐색, 사실확인형 검색, 조사형 탐색, 조사형 검색 등 4가지 형태로 유형화하였다.

• Asian Journal for Public Opinion Research
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• 제9권1호
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• pp.48-82
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• 2021

This research article attempts to understand the current situation of fake news on social media in India. The study focused on four characteristics of fake news based on four research questions: subject matter, presentation elements of fake news, debunking tool(s) or technique(s) used, and the social media site on which the fake news story was shared. A systematic sampling method was used to select a sample of 90 debunked fake news stories from two Indian fact-checking websites, Alt News and Factly, from December 2019 to February 2020. A content analysis of the four characteristics of fake news stories was carefully analyzed, classified, coded, and presented. The results show that most of the fake news stories were related to politics in India. The majority of the fake news was shared via a video with text in which narrative was changed to mislead users. For the largest number of debunked fake news stories, information from official or primary sources, such as reports, data, statements, announcements, or updates were used to debunk false claims.

• 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.