• Asia pacific journal of information systems
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• 제29권4호
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• pp.591-614
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• 2019

With the proliferation of social media, it has become easier for people to spread rumors online, which can aggravate the issues arising from online rumors. There are many individuals and organizations that are adversely affected by malicious online rumors. Despite their importance, there has been little research into why and how people spread rumors online, thus inhibiting the understanding of factors that affect the spreading of online rumors. With attention seeking to address this gap, this paper draws upon the dual process theory and the de-individuation theory to develop a theoretical model of factors affecting the spreading of an online rumor, and then empirically tests it using survey data from 211 individuals about a specific rumor. The results indicate that the perceived credibility of the rumor affects the individuals' attitudes toward spreading it, which consequently affects the rumor spreading behavior. Vividness, confirmation of prior beliefs, argument strength, and source credibility positively influence the perceived credibility of online rumors. Finally, anonymity moderates the relationship between attitude toward spreading online rumors and the spreading behavior.

• Asia pacific journal of information systems
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• 제30권1호
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• pp.1-20
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• 2020

Malicious rumors often emerge online. However, few studies have examined why people spread online rumors. Recognizing that spreading online rumors is not only rational, but also emotional, this paper provides insights into the behavior of online rumor spreading using the cognitive emotion theory. The results show that perceived credibility of online rumors enhances both positive and negative emotions. However, positive emotions affect neither attitude nor behavior, whereas negative emotions affect both aspects of the spreading of online rumors. The results also indicate that prior positive attitude toward object influences negative emotions. Issues involvement moderates the relationship between attitude and behavior.

• 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.

• 한국컴퓨터정보학회논문지
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• 제21권1호
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• pp.139-146
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• 2016

In this paper, we analyze the information asymmetry among investors in online trading environment using rumors which are collected in the Korean stock market for the eleven-year period between January 2004 and December 2014. We find that cumulative abnormal return of sample firms is negative and statistically significant, indicating that a significant fall of the stock price starts before the online disclosure, suggesting that the rumors were reflected in the stock price to a significant extent. Furthermore, individual investors show net purchases on firms prior to disclosure while institutional investors show net sales, showing that individual investors trade unfavorably vis-$\grave{a}$-vis institutional investors. This phenomenon is more evident for the KOSDAQ. This result confirms that the information asymmetry exists between individual and institutional investors in online trading environment.

• Journal of Preventive Medicine and Public Health
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• 제53권3호
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• pp.171-174
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• 2020

The coronavirus disease 2019 (COVID-19) pandemic has not only caused significant challenges for health systems all over the globe but also fueled the surge of numerous rumors, hoaxes, and misinformation, regarding the etiology, outcomes, prevention, and cure of the disease. Such spread of misinformation is masking healthy behaviors and promoting erroneous practices that increase the spread of the virus and ultimately result in poor physical and mental health outcomes among individuals. Myriad incidents of mishaps caused by these rumors have been reported globally. To address this issue, the frontline healthcare providers should be equipped with the most recent research findings and accurate information. The mass media, healthcare organization, community-based organizations, and other important stakeholders should build strategic partnerships and launch common platforms for disseminating authentic public health messages. Also, advanced technologies like natural language processing or data mining approaches should be applied in the detection and removal of online content with no scientific basis from all social media platforms. Furthermore, these practices should be controlled with regulatory and law enforcement measures alongside ensuring telemedicine-based services providing accurate information on COVID-19.

• 정보과학회 논문지
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• 제44권7호
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• pp.680-685
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• 2017

온라인 소셜미디어의 등장으로 방대한 사용자 데이터가 수집되고 이는 루머의 탐지와 같은 복잡하고 도전적인 사회 문제를 자료 기반 기법으로 해결할 수 있게끔 한다. 최근 딥러닝 기반 모델들이 이러한 문제를 해결하기 위한 빠르고 정확한 기법 중의 하나로서 소개되었다. 하지만 기존에 제시된 모델들은 전파 종료 후 작동하거나 오랜 관찰기간을 필요로 하여 활용성이 제한된다. 이 연구에서는 초기 소량 데이터만을 활용하는 recurrent neural networks (RNNs) 기반의 빠른 루머 분류 알고리즘을 제안한다. 제시된 모델은 소셜미디어 스트림을 시계열 자료로 변환하여 사용하며, 이 때 시계열 데이터는 팔로워 수와 같이 정보 전파자 관련 정보는 물론 주어진 컨텐츠에서 추론한 언어심리학적 감성의 점수로 구성된다. 수백만의 트윗을 포함하는 498개의 실제 루머 및 494개의 비루머 사례 분석을 통해 이 연구는 제안하는 RNN 기반 모델이 초기 30개의 트윗 만으로도 (초기 수시간) 0.74 F1의 높은 성능을 보임을 확인한다. 이러한 결과는 실제 응용가능한 수준의 빠르고 효율적인 루머 분류 알고리즘 개발의 초석이 된다.

• 경영정보학연구
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• 제15권2호
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• pp.21-40
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• 2013

오늘날 정보기술의 발달, 특히 소셜미디어의 확산에 따라 온라인 루머와 악성 댓글이 증가하는 추세이며 그에 따른 사회 분열이 가속화되고 있다. 이러한 사회 분열 현상이 사회에 끼치는 부정적 영향력은 매우 크기 때문에, 이에 대처하고 사회 통합을 이끌어 낼 수 있는 방안이 시급하다. 따라서 본 연구에서는 소셜미디어를 기반으로 신뢰를 기반으로 소통하는 사회로 나아갈 수 있는 정책적 방안을 마련하고자 하였다. 또한 이를 위해 소셜미디어 환경에서 사회적 자본의 구성 요소들을 중심으로 인과관계 다이어그램(CLD)을 도출하여 새로운 사회 문화 변화 현상 분석 및 사회 통합을 위한 대응정책을 제안하였다.

• Journal of Information Technology Applications and Management
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• 제20권4호
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• pp.191-205
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• 2013

Recently, the topic of predicting interpersonal trust in online social networks is receiving considerable attention, because trust plays a critical role in controlling the spread of distorted information and vicious rumors, as well as reducing uncertainties and risk from unreliable users in social networks. Several trust prediction models have been developed on the basis of transitivity and composability properties of trust; however, it is hard to find empirical studies on whether and how transitivity and composability properties of trust are operated in real online social networks. This study aims to predict interpersonal trust between two unknown users in social networks and verify the proposition on whether and how transitivity and composability of trust are operated in social networks. For this purpose, we chose three social network sites called FilmTrust, Advogato, and Epinion, which contain explicit trust information by their users, and we empirically investigated the proposition. Experimental results showed that trust can be propagated farther and farther along the trust link; however, when path distance becomes distant, the accuracy of trust prediction lowers because noise is activated in the process of trust propagation. Also, the composability property of trust is operated as we expected in real social networks. However, contrary to our expectations, when the path is synthesized more during the trust prediction, the reliability of predicted trust did not tend to increase gradually.

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1422-1437
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• 2019

With the rapid development of the Internet and the Mobile Internet, social communication based on the network has become a life style for many people. WeChat is an online social platform, for about one billion users, therefore, it is meaningful to study the spreading and evolution mechanism of the rumor on the WeChat social circle. The Rumor was injected into the WeChat social circle by certain individuals, and the communication and the evolution occur among the nodes within the circle; after the refuting-rumor-information injected into the circle, subsequently,the density of four types of nodes, including the Susceptible, the Latent, the Infective, and the Recovery changes, which results in evolving the WeChat social circle system. In the study, the evolution characteristics of the four node types are analyzed, through construction of the evolution equation. The evolution process of the rumor injection and the refuting-rumor-information injection is simulated through the structure of the virtual social network, and the evolution laws of the four states are depicted by figures. The significant results from this study suggest that the spreading and evolving of the rumors are closely related to the nodes degree on the WeChat social circle.

• 한국산업정보학회논문지
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• 제29권1호
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• pp.15-29
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• 2024

최근 정보통신 기술의 발전으로 인해 유튜브는 이용자 자신의 관심사와 경험을 담은 콘텐츠를 만들어 공유함으로써 새로운 문화 현상을 창출하고 확산시키는 강력한 온라인 공간이 되었다. 특히, 제조 분야는 소비자의 직접적인 접촉도가 상대적으로 거의 없었다는 이유로 소셜미디어에 대한 연구가 거의 없었다. 기업에 있어 유튜브는 자사 제품 및 브랜드의 홍보와 같이 경영에 있어 긍정적인 효과를 가질 수 있지만, 그와 반대로 루머나 잘못된 정보로 인해 생산 단절과 같은 제조 리스크가 발생할 수 있다. 그렇기 때문에 기업은 유튜브 동영상의 특징에 따라 구전 확산에 따른 특징을 살펴볼 필요가 있다. 이에 본 연구는 유튜브에서 전기 자동차의 결함을 다루고 있는 동영상을 추출하여 구독자 수 및 조회 수에 따라 어떤 확산 네트워크 구조를 갖고 있는지를 네트워크 통계 분석을 통해서 사시점을 규명하고자 한다.