• Asia pacific journal of information systems
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• v.29 no.4
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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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• v.30 no.1
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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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• v.8 no.3
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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.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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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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• v.53 no.3
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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.

• Journal of KIISE
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• v.44 no.7
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• pp.680-685
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• 2017

The emergence of online media and their data has enabled data-driven methods to solve challenging and complex tasks such as rumor classification problems. Recently, deep learning based models have been shown as one of the fastest and the most accurate algorithms to solve such problems. These new models, however, either rely on complete data or several days-worth of data, limiting their applicability in real time. In this study, we go beyond this limit and test the possibility of super early rumor detection via recurrent neural networks (RNNs). Our model takes in social media streams as time series input, along with basic meta-information about the rumongers including the follower count and the psycholinguistic traits of rumor content itself. Based on analyzing millions of social media posts on 498 real rumors and 494 non-rumor events, our RNN-based model detected rumors with only 30 initial posts (i.e., within a few hours of rumor circulation) with remarkable F1 score of 0.74. This finding widens the scope of new possibilities for building a fast and efficient rumor detection system.

• Information Systems Review
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• v.15 no.2
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• pp.21-40
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• 2013

Because of the recent development of information technology, especially the popularization of social media, false rumors and abusive comments on social media have increased, which has accelerated conflicts in online society. Since these conflicts lead to significant negative influences on online society, it is important to figure out how to cope with the conflicts and integrate the society. Thus, this study provides effective political implications that will advance the society where people can communicate based on trust in social media. In addition, we suggest new sociocultural change phenomenon and social integration by drawing the Causal Loop Diagram focusing on the dimension of social capital in social media environment.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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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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• v.15 no.6
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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 Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.15-29
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• 2024

With recent advances in information and communication technology, YouTube has become a powerful online space for users to create and share content about their interests and experiences, creating new cultural phenomena. In particular, there needs to be more research on social media in the manufacturing sector because, unlike distribution and retail, there has been relatively little direct contact with consumers. YouTube can positively affect firms' performance by promoting products and brands. On the other hand, it can also cause risks, such as production disruption due to rumors or misinformation. Thus, it is necessary for firms to examine how information about an electric automobile defects spreads on YouTube according to the number of subscribers and views through statistical network analysis.