SEDIS—A Rumor Propagation Model for Social Networks by Incorporating the Human Nature of Selection

Govindankutty, Sreeraag; Shynu, P G

doi:10.3390/systems11010012

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Human: On the web and digital platforms, you can find analyses about the promotion of food products designed for children, along with tactics used in the virtual environment to market them (Mota-Castillo et al, 2023). Likewise, in the human domain, social media is used as spaces to exchange opinions, emotions, data, personal experiences, and even rumors (Govindankutty & Gopalan, 2023).…”

Section: A Within the Motor Themes Ismentioning

confidence: 99%

Mapping digital marketing research in social networks: A short-term bibliometric analysis (2018-2023)

2024

RCS

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This study delved into the world of Digital Marketing in social networks, exploring its evolution from 2018 to April 2023 through the prism of the Scopus database. Using a quantitative methodology with a bibliometric approach, 368 documents were examined using statistical tools such as VOSviewer, RStudio and Microsoft Excel. The results paint a picture where the annual document production shows a steady decline of -3.34%, attributed to saturation, changing algorithms and mutable preferences. With the participation of 81 countries, Spain, USA, UK, India, and Indonesia emerge as prominent players, while 26 subject areas, led by Business, Computer Science and Social Sciences, top the subject matter. Documentary diversity is reflected in articles (76.36%), papers (15.22%) and reviews (3.80%). In this scenario, nine authors, including Civelek et al. (2020), Dwivedi et al. (2020, and Saura (2021), stand out. The exploration of trends took the form of a Thematic Map with specific sections, while future inquiries are oriented towards resource theory, destination marketing, social network platforms, diet advertising and social network analysis. Overall, this study not only provides insight into contemporary scientific production, but also points to fruitful directions for future research.

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Section: A Within the Motor Themes Ismentioning

confidence: 99%

Mapping digital marketing research in social networks: A short-term bibliometric analysis (2018-2023)

2024

RCS

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“…Rumors are unverified information often shared through word of mouth or social media. While these can be harmless, they can cause panic or misinformation if the audience takes accurate information [43]. An example is the pandemic-related rumors and conspiracy theories that associate companies or certain influential investors as the causes of the healthcare crisis [22,44,45].…”

Section: Rumorsmentioning

confidence: 99%

Fake News in Marketing

Silva¹,

Rosário²,

Gestoso³

et al. 2023

Preprint

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Fake news was conventionally assumed to mean fabricated information published in newspapers and other mass media. It was mostly done to increase the paper's sales and was unlikely to have long-term impacts on society. Fake news in marketing concerns intentionally delivering false or misleading information regarding a product or service to influence consumer behavior. Despite the severity of these impacts, we noted that there is very little scientific research on fake news in marketing and consumer behavior. This systematic literature review (LRSB) aims to bridge this knowledge gap by searching and analysing relevant publications on "fake news in marketing" and synthesising data from 117 relevant studies, providing a framework for marketers and business leaders to reduce the spread and impact of online misinformation. The main highlights identify that the implementation and use of social media, shaping a presence on the internet, diffusion, and consumption of fake news have become a serious challenge and a dominant problem in the marketing sector, especially with the growth of digital marketing. Strategically, fake news spread can be categorized into various types, and the use of these tactics can produce short-term benefits such as increased engagement and instant sales, but they can also have severe long-term costs such as reputational damage, loss of revenues and market share, and discouraged investors and shareholders.

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“…By considering the human aspect of selection, the SEDIS (Susceptible -Exposed -Doubter -Infected -Susceptible) model tends to be a more realistic model of rumor propagation than classic epidemic models [19]. In traditional epidemic models, all users are thought to be equally likely to be exposed to a rumor.…”

Section: Introductionmentioning

confidence: 99%

Modeling Rumor Spread and Influencer Impact on Social Networks

Govindankutty,

Padinjappurathu Gopalan

2023

IEEE Access

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Social networks act as an indispensable component in the lives of individuals. However, misinformation and fake news are critical challenges in the digital world as people get persuaded towards false information. Though several fake news detection algorithms emerged, epidemic modeling is crucial in understanding the dissemination of fake news, which helps the policyholders to adopt control mechanisms to prevent the curb of infection within the networks. We propose a mathematical model of rumor spread by considering the human nature of selection and social influence within social networks by analyzing the stiffness of different global communities. The positivity of the model was mathematically proved, which proves the validity of the model within the real world. Our real-world data analysis showcases the possibility of a significant increase in fake news and misinformation within online digital networks during the COVID-19 pandemic. A comparative study using real-world data by extracting tweets shows that the proposed model outperforms the existing model. The significance of influencers in digital networks in disseminating rumors is discussed using the proposed model. The results can be used to analyze the impact of misinformation in different communities, which can help the policyholders implement necessary intervention mechanisms at the right time.

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SEDIS—A Rumor Propagation Model for Social Networks by Incorporating the Human Nature of Selection

Cited by 7 publications

References 25 publications

Mapping digital marketing research in social networks: A short-term bibliometric analysis (2018-2023)

Mapping digital marketing research in social networks: A short-term bibliometric analysis (2018-2023)

Fake News in Marketing

Modeling Rumor Spread and Influencer Impact on Social Networks

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