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International Symposium: From Data to Models and Back

DataMod 2020: From Data to Models and Back pp 72–86Cite as

Discovering the Impact of Notifications on Social Network Addiction

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 12611)

Abstract

Addiction is a complex phenomenon, coming from environmental, biological, and psychological causes. It is defined as a natural response of the body to external stimuli that become compulsive needs. From the biological point of view, the brain has the central role: many neural circuits and, above all, the Dopamine System, are involved in the addiction process. Over the last decade, social network communication has become an increasingly addictive activity, for which users appear to engage in social media excessively and/or compulsively. In this work, we show that the current online social networks’ notifications system triggers addictive behaviors. We prove our hypothesis simulating the mathematical modeling of the Dopamine System on real interactions among members of a set of 18 Facebook groups. In line with recent psychological studies, we find that the addicted users show a high frequency of social interactions on the platform.

Keywords

  • Social networks
  • Computational model
  • Internet addiction
  • Facebook groups

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Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 56127, Pisa, Italy

    Lucia Nasti, Andrea Michienzi & Barbara Guidi

Authors
  1. Lucia Nasti
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  2. Andrea Michienzi
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  3. Barbara Guidi
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Corresponding author

Correspondence to Lucia Nasti .

Editor information

Editors and Affiliations

  1. University of St Andrews, St Andrews, UK

    Juliana Bowles

  2. ISTI-CNR, Pisa, Italy

    Giovanna Broccia

  3. ISTI-CNR, Pisa, Italy

    Mirco Nanni

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Nasti, L., Michienzi, A., Guidi, B. (2021). Discovering the Impact of Notifications on Social Network Addiction. In: Bowles, J., Broccia, G., Nanni, M. (eds) From Data to Models and Back. DataMod 2020. Lecture Notes in Computer Science(), vol 12611. Springer, Cham. https://doi.org/10.1007/978-3-030-70650-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-70650-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70649-4

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