A rumor reversal model of online health information during the Covid-19 epidemic

Wang, Xiwei; Li, Yueqi; Li, Jiaxing; Liu, Yutong; Qiu, Chengcheng

doi:10.1016/j.ipm.2021.102731

Cited by 47 publications

(23 citation statements)

References 72 publications

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“…Rumors behave similar to an infectious disease, see [37]. We introduce five compartments (see figure 2) similar to [38].…”

Section: Information Diffusion Submodelmentioning

confidence: 99%

Modeling the influence of the information domain on countermeasure effectiveness in case of COVID-19

Fischer,

Gerwald,

Lajos

et al. 2023

J. Phys.: Conf. Ser.

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A common way to model an epidemic — restricted to contagion aspects only — is a modification of the Kermack-McKendrick SIR Epidemic model (SIR model) with differential equations. (Mis-)Information about epidemics may influence the behavior of the people and thus the course of epidemics as well. We have thus coupled an extended SIR model of the COVID-19 pandemic with a compartment model of the (mis-)information-based attitude of the population towards epidemic countermeasures. The resulting combined model is checked concerning basic plausibility properties like positivity and boundedness. It is calibrated using COVID-19 data from RKI and attitude data provided by the COVID-19 Snapshot Monitoring (COSMO) study. The values of parameters without corresponding observation data have been determined using an L2 -fit under mild additional assumptions. The predictions of the calibrated model are essentially in accordance with observations. An uncertainty analysis of the model shows, that our results are in principle stable under measurement errors. We also assessed the scale, at which specific parameters can influence the evolution of epidemics. Another result of the paper is that in a multi-domain epidemic model, the notion of controlled reproduction number has to be redefined when being used as an indicator of the future evolution of epidemics.

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“…Rumors behave similar to an infectious disease, see [37]. We introduce five compartments (see figure 2) similar to [38].…”

Section: Information Diffusion Submodelmentioning

confidence: 99%

Modeling the influence of the information domain on countermeasure effectiveness in case of COVID-19

Fischer,

Gerwald,

Lajos

et al. 2023

J. Phys.: Conf. Ser.

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“…One of the consequences of the technological revolution with information technology as its core and its wide application is the mass and diversified ways of knowledge and information dissemination. Faced with a huge amount of information, consumers have more active choices while being overwhelmed ( Wang et al, 2021 ). In the traditional mode of communication, mass media, such as television, radio, newspapers, and magazines, as the disseminators of information, control the contents, ways, and channels of information dissemination, and become the main controller of information, while the public can only exist as receivers of information and play a completely passive role.…”

Section: Related Workmentioning

confidence: 99%

Analysis of consumer product preference and news media based on data mining technology

Wang

Fan

2022

Front. Psychol.

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In order not to be eliminated by the market, enterprises must face various consumer preferences, design products that meet consumer preferences, and enhance competitiveness. This paper combines on-the-spot marketing to study the product preferences of consumers and the personality characteristics of media hosts. This paper introduces the data mining technology of news media into the research of consumer’s preference for products. Based on the comprehensive use of media mining technology, customer research theory, and product background and foundation, the specific process of influencing consumers’ product preference is established. It can be seen from the study that the personality of the anchor has a great relationship with the consumption level of consumers and the sales rate of products, with an impact of 78.53%. Through this study, we can see that there is a certain relationship between consumers’ product preference and anchoring personality. Studying the basic characteristics of the phenomenon live broadcast commodity marketing model has important theoretical value for analyzing the live broadcast commodity marketing model. It points out the direction for the scientific, healthy, and sustainable development of the future live broadcast commodity marketing mode.

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“…For instance, Hui et al [ 7 ] adopted the epidemic-like model to examine the spread process of misinformation on social media. To understand the process of online rumor spreading, Wang et al [ 8 ] built on the SIR epidemic model to simulate the rumor propagation-reversal process. Other researchers also explored the propagation patterns of misinformation on social media platforms [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

What Drives People to Share Misinformation on Social Media during the COVID-19 Pandemic: A Stimulus-Organism-Response Perspective

2022

IJERPH

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(1) Background: Misinformation is prevalent on social media in the age of COVID-19, exacerbating the threat of the pandemic. Uncovering the processes underlying people’s misinformation sharing using social media assists people to cope with misinformation during the COVID-19 pandemic. This study extends the stimulus-organism-response framework to examine how individuals’ social media dependency relates to their misinformation sharing behavior, with a focus on the underlying processes. (2) Methods: A total of 393 valid questionnaires were collected using a survey method to test the proposed research model. (3) Results: The results demonstrate that informational dependency and social dependency engender both positive and negative cognitive states, namely perceived information timeliness, perceived socialization and social overload, which then invoke positive as well as negative affect. What is more, the results show that both positive affect and negative affect can engender misinformation sharing. (4) Conclusions: Theoretically, this study uncovers the processes that lead to misinformation sharing on social media during the COVID-19 pandemic. Practically, this study provides actionable guidelines on how to manage social media usage and social media content to cope with misinformation sharing during the pandemic.

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A rumor reversal model of online health information during the Covid-19 epidemic

Cited by 47 publications

References 72 publications

Modeling the influence of the information domain on countermeasure effectiveness in case of COVID-19

Modeling the influence of the information domain on countermeasure effectiveness in case of COVID-19

Analysis of consumer product preference and news media based on data mining technology

What Drives People to Share Misinformation on Social Media during the COVID-19 Pandemic: A Stimulus-Organism-Response Perspective

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