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Privacy Paradox in Social Media: A System Dynamics Analysis

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Computational Science – ICCS 2022 (ICCS 2022)

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Abstract

The term ‘privacy paradox’ refers to the apparent inconsistency between people’s concerns about their privacy and their actual privacy behaviour. Although several possible explanations for this phenomenon have been provided so far, these assume that (1) all people share the same privacy concerns and (2) a snapshot at a given point in time is enough to explain the phenomenon. To overcome these limitations, this article presents a system dynamics simulation model that considers the diversity of privacy concerns during the process of social media adoption and identifies the types of situations in which the privacy paradox emerges. The results show that (1) the least concerned minority can induce the more concerned majority to adopt social media and (2) even the most concerned minority can be hindered by the less concerned majority from discarding social media. Both (1) and (2) are types of situations that reflect the privacy paradox.

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Acknowledgements

The authors would like to thank Pekka Nikander for his insightful comments.

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Correspondence to Ektor Arzoglou .

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Appendix: Model Equations and Parameter Values

Appendix: Model Equations and Parameter Values

Table 3. Model equations and parameter values

The model equations and parameter values are shown in Table 3. In the equations, subscript w refers to the user group (p: Pragmatists, f: Fundamentalists, u: Unconcerned). The model includes formulations to ensure that users cannot be added or removed spontaneously (i.e. mass balance) and that stock variables stay non-negative. For clarity, these have been omitted from the equations shown in Table 3. For details of the formulations and to ensure the replicability of the simulation results, the simulation model Vensim file is openly available upon request.

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Arzoglou, E., Kortesniemi, Y., Ruutu, S., Elo, T. (2022). Privacy Paradox in Social Media: A System Dynamics Analysis. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_47

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  • DOI: https://doi.org/10.1007/978-3-031-08751-6_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08750-9

  • Online ISBN: 978-3-031-08751-6

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