• Korean System Dynamics Review
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• v.14 no.4
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• pp.5-35
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• 2013

The study examines the play theory based internet rumor process by using simulating tools, Vensim, which offer a new theoretical basis from which to explore complex adaptive social system. Internet rumor is not a simple linear diffusion process, but a complex interaction behavior between the actors of production and diffusion. Rumor actors consist of two type of diffusion, which is rumor mongers and playful mongers. These two type of mongers make the internet rumor as collective system. Playful mongers play strategically to maximize playfulness. Internet rumor as play is consequence of collective framing constituted by dynamic interaction and playfulness. The networking space spreading internet rumor function as a playground which mobilize play rule, ignoring fact based framing. Rumor as paly, even though it turns out to be a false and loses the public attentions rumor sustains the game play function which makes the rumor without natural extinction. The study proves that playful mongers is a main actors in rumor play ground.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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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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• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

• Journal of Information Processing Systems
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• v.15 no.4
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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.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.113-125
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• 2017

This study focused on the difference between the volume of rumors and types of rumors via social media based on case studies and network analysis. Rumors are classified into core statement, peripheral statement, core gossip, and peripheral gossip based on their factuality and influence. The diffusion of opinions via social media is classified into dispersive mob, solid mob, dispersive minority, and solid minority based on the volume of node and its interactivity. The results of this study show that, core gossip is actively dispersed. Users are more interested in the influence of rumor than in rumors that appear the be factual. This study highlighted what the role of government when rumor is diffusing and in the aspect of message what the characteristic of rumor.

• Asia Marketing Journal
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• v.14 no.2
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• pp.65-96
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• 2012

Brand has received much attention from considerable marketing research. When consumers consume product or services, they are exposed to a lot of brand related stimuli. These contain brand personality, brand experience, brand identity, brand communications and so on. A special kind of new crisis occasionally confronting companies' brand management today is the brand related rumor. An important influence on consumers' purchase decision making is the word-of-mouth spread by other consumers and most decisions are influenced by other's recommendations. In light of this influence, firms have reasonable reason to study and understand consumer-to-consumer communication such as brand rumor. The importance of brand rumor to marketers is increasing as the number of internet user and SNS(social network service) site grows. Due to the development of internet technology, people can spread rumors without the limitation of time, space and place. However relatively few studies have been published in marketing journals and little is known about brand rumors in the marketplace. The study of rumor has a long history in all major social science. But very few studies have dealt with the antecedents and consequences of any kind of brand rumor. Rumor has been generally described as a story or statement in general circulation without proper confirmation or certainty as to fact. And it also can be defined as an unconfirmed proposition, passed along from people to people. Rosnow(1991) claimed that rumors were transmitted because people needed to explain ambiguous and uncertain events and talking about them reduced associated anxiety. Especially negative rumors are believed to have the potential to devastate a company's reputation and relations with customers. From the perspective of marketer, negative rumors are considered harmful and extremely difficult to control in general. It is becoming a threat to a company's sustainability and sometimes leads to negative brand image and loss of customers. Thus there is a growing concern that these negative rumors can damage brands' reputations and lead them to financial disaster too. In this study we aimed to distinguish antecedents of brand rumor transmission and investigate the effects of brand rumor characteristics on rumor spread intention. We also found key components in personal acceptance of brand rumor. In contextualist perspective, we tried to unify the traditional psychological and sociological views. In this unified research approach we defined brand rumor's characteristics based on five major variables that had been found to influence the process of rumor spread intention. The five factors of usefulness, source credibility, message credibility, worry, and vividness, encompass multi level elements of brand rumor. We also selected product involvement as a control variable. To perform the empirical research, imaginary Korean 'Kimch' brand and related contamination rumor was created and proposed. Questionnaires were collected from 178 Korean samples. Data were collected from college students who have been experienced the focal product. College students were regarded as good subjects because they have a tendency to express their opinions in detail. PLS(partial least square) method was adopted to analyze the relations between variables in the equation model. The most widely adopted causal modeling method is LISREL. However it is poorly suited to deal with relatively small data samples and can yield not proper solutions in some cases. PLS has been developed to avoid some of these limitations and provide more reliable results. To test the reliability using SPSS 16 s/w, Cronbach alpha was examined and all the values were appropriate showing alpha values between .802 and .953. Subsequently, confirmatory factor analysis was conducted successfully. And structural equation modeling has been used to analyze the research model using smartPLS(ver. 2.0) s/w. Overall, R² of adoption of rumor is .476 and R² of intention of rumor transmission is .218. The overall model showed a satisfactory fit. The empirical results can be summarized as follows. According to the results, the variables of brand rumor characteristic such as source credibility, message credibility, worry, and vividness affect argument strength of rumor. And argument strength of rumor also affects rumor intention. On the other hand, the relationship between perceived usefulness and argument strength of rumor is not significant. The moderating effect of product involvement on the relations between argument strength of rumor and rumor W.O.M intention is not supported neither. Consequently this study suggests some managerial and academic implications. We consider some implications for corporate crisis management planning, PR and brand management. This results show marketers that rumor is a critical factor for managing strong brand assets. Also for researchers, brand rumor should become an important thesis of their interests to understand the relationship between consumer and brand. Recently many brand managers and marketers have focused on the short-term view. They just focused on strengthen the positive brand image. According to this study we suggested that effective brand management requires managing negative brand rumors with a long-term view of marketing decisions.

• Korean journal of communication and information
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• v.84
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• pp.152-188
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• 2017

This study stereotyped the media on the basis of ideological inclinations and media types and explored the news coverage through word analysis, network analysis, and frame analysis. There was no difference between conservative media and progressive media in terms of the amount of news. The conservative mainstream media considered the THAAD rumor as an unnecessary misunderstanding and a rumor based conflict of the south-south. The progressive mainstream media mentioned much about Hwang Gyoan, external influences, and lies and highlighted the government's opinion that there was external influence that spread a vicious rumor. Conservative media mentioned on the bringing about social disturbance and in case of progressive media mentioned social disturbance, and progressive media mentioned the responsibility of government and the attitude of conservative media about the diffusion of the rumor. In conclusion the press framed the THAAD rumor on the basis of their ideological inclinations instead of the role of journalist.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.7
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• pp.275-288
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• 2019

In this paper, we consider a rumor source inference problem when sufficiently many nodes heard the rumor in the network. This is an important problem because information spread in networks is fast in many real-world phenomena such as diffusion of a new technology, computer virus/spam infection in the internet, and tweeting and retweeting of popular topics and some of this information is harmful to other nodes. This problem has been much studied, where it has been shown that the detection probability cannot be beyond 31% even for regular trees if the number of infected nodes is sufficiently large. Motivated by this, we study the impact of query that is asking some additional question to the candidate nodes of the source and propose budget assignment algorithms of a query when the network administrator has a finite budget. We perform various simulations for the proposed method and obtain the detection probability that outperforms to the existing prior works.

• The Journal of Information Systems
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• v.23 no.1
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• pp.111-138
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• 2014

As the Internet is widely used worldwide, Cyber world has become a part of daily life. On the other hand, the adverse effects of Internet, such as abusive comments, illegal harmful contents, rumor diffusion, infringement on a right have emerged with the increase. Researchers recognized the important of cyber ethics as an effective means of coping with the adverse effects on Internet. But, Little empirical research has been related to cyber ethics in management information systems. Thus, the purpose of this research is to identify multidimensional motivation factors that cyber ethics awareness and compliance behavior Intention. For this, this study investigated the factors that might influence Internet users to compliance cyber ethics. Using university students as a sample, one hundred and ninety six are collected for further analysis. The results of this study are as follows; First, self-esteem, Moral Obligation, self-control, cyber ethics education and Social Influence are significantly related to cyber ethics awareness, while punishment is not significantly related. Second, cyber ethics awareness have a positive effect on cyber ethics compliance behavior Intention. Third, the moderating effect of ego strength is also significant. This study is significant in that it established a behavior model to understand the compliance of cyber ethics with internet user in Korea.