Modeling the dynamics of user adoption and abandonment in online social networks

We introduce a new compartmental model aimed at examining the dynamics of user adoption and abandonment within online social networks. Our model encompasses two abandonment dynamics: infectious abandonment caused by internal influences from individuals who have already left the network and noninfectious abandonment triggered by mass media, advertisements, or the introduction of new products. We verify the model's validity and explore the existence and stability of its equilibria. To confirm our theoretical framework, we conduct extensive numerical simulations. We demonstrate the model's effectiveness by fitting it to historical data on Facebook's daily active users. Finally, we utilize the derived model parameters to forecast the future numbers of active users on Facebook.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2024-08-20
DOI
https://doi.org/10.1002/mma.10413
Citations
5
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.41

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article

Modeling the dynamics of user adoption and abandonment in online social networks

Min Wang, Lingju Kong, Noah Kimmel
5 citations
Mathematical Methods in the Applied Sciences
Complex Network Analysis Techniques
1.41
article

Modeling the dynamics of user adoption and abandonment in online social networks

Min Wang, Lingju Kong, Noah Kimmel
article en
5 citations

Abstract

We introduce a new compartmental model aimed at examining the dynamics of user adoption and abandonment within online social networks. Our model encompasses two abandonment dynamics: infectious abandonment caused by internal influences from individuals who have already left the network and noninfectious abandonment triggered by mass media, advertisements, or the introduction of new products. We verify the model's validity and explore the existence and stability of its equilibria. To confirm our theoretical framework, we conduct extensive numerical simulations. We demonstrate the model's effectiveness by fitting it to historical data on Facebook's daily active users. Finally, we utilize the derived model parameters to forecast the future numbers of active users on Facebook.

Mathematical Methods in the Applied SciencesVol. 48(2)
Kennesaw State University (US), University of Tennessee at Chattanooga (US)
University of Tennessee at Chattanooga
Openalex Percentile: Top 19%
Complex Network Analysis Techniques
1.41
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