• 디지털융복합연구
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• 제18권1호
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• pp.45-52
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• 2020

온라인 루머는 피해자에게 극심한 정신적 스트레스와 이미지 손실을 발생시킨다. 온라인 루머 관련 선행 연구들은 온라인 루머가 온라인 공간에서 발생하는 현상임에도 불구하고 온라인 환경 요인을 크게 고려하지 않았다. 따라서 본 연구는 온라인 루머에 대한 온라인 특성의 영향력을 발견하고자 하였다. 본 연구는 인지된 익명성, 사회적 실재감 부족, 인지된 전파성을 온라인 특성으로 고려하였으며, 온라인 특성이 온라인 루머에 대한 태도를 거쳐 온라인 루머 행동으로 이어지는 연구 모델을 수립하고 실증하였다. 본 연구는 설문조사를 기반으로 소셜 네트워크 사용자 201명의 표본을 확보하였으며, PLS 분석 프로그램을 통해 연구 모델을 검증 하였다. 연구 결과, 인지된 익명성과 인지된 전파성은 온라인 루머에 대한 태도를 거쳐 온라인 루머 행동에 영향을 미치는 것으로 나타났다. 반면에 사회적 실재감 부족은 유의하지 않은 것으로 나타났다. 본 연구의 발견은 개인의 온라인 루머 행동이 온라인 특성에 의해서 발생할 수 있다는 사실을 제공한다. 본 연구는 온라인 루머 행동에 대해 인지된 익명성과 인지된 전파성의 역할을 주목할 필요성을 제시한다.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• 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.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.853-864
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• 2019

Microblogging services (such as Twitter) are the representative information communication networks during the Web 2.0 era, which have gained remarkable popularity. Weibo has become a popular platform for information dissemination in online social networks due to its large number of users. In this study, a microblog information dissemination model is presented. Related concepts are introduced and analyzed based on the dynamic model of infectious disease, and new influencing factors are proposed to improve the susceptible-infective-removal (SIR) information dissemination model. Correlation analysis is conducted on the existing information dissemination risk and the rumor dissemination model of microblog. In this study, web hyper is used to model rumor dissemination. Finally, the experimental results illustrate the effectiveness of the method in reducing the rumor dissemination of microblogs.

• 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.

• 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.

• 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 Information Technology Applications and Management
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• 제19권2호
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• pp.197-215
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• 2012

SNS has been recognized as a means of expanding social capital by promoting interaction and efficient communication among users. On the other hand, there are serious concerns on negative side of social network which is often called epidemics. Trust plays a critical role in controlling the spread of distorted information and vicious rumor as well as reducing uncertainties and risk from unreliable users in social network. This study focuses on what the antecedents of interpersonal trust are in social network. We performed online survey from 252 Twitter users and tested candidate antecedents which are chosen from previous literature. As a result, propensity to trust of trustor, ability and sincerity of trustee, intimacy between trustor and trustee significantly affected to the interpersonal trust in Twitter.

• Journal of Information Science Theory and Practice
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• 제7권2호
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• pp.40-53
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• 2019

Although fake news has been present in human history at any time, nowadays, with social media, deceptive information has a stronger effect on society than before. This article answers two research questions, namely (1) Is the dissemination of fake news supported by machines through the automatic construction of filter bubbles, and (2) Are echo chambers of fake news manmade, and if yes, what are the information behavior patterns of those individuals reacting to fake news? We discuss the role of filter bubbles by analyzing social media's ranking and results' presentation algorithms. To understand the roles of individuals in the process of making and cultivating echo chambers, we empirically study the effects of fake news on the information behavior of the audience, while working with a case study, applying quantitative and qualitative content analysis of online comments and replies (on a blog and on Reddit). Indeed, we found hints on filter bubbles; however, they are fed by the users' information behavior and only amplify users' behavioral patterns. Reading fake news and eventually drafting a comment or a reply may be the result of users' selective exposure to information leading to a confirmation bias; i.e. users prefer news (including fake news) fitting their pre-existing opinions. However, it is not possible to explain all information behavior patterns following fake news with the theory of selective exposure, but with a variety of further individual cognitive structures, such as non-argumentative or off-topic behavior, denial, moral outrage, meta-comments, insults, satire, and creation of a new rumor.