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Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach

More than three billion users are currently on one of Meta’s online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in...

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Autores principales: Marengo, Davide, Montag, Christian, Mignogna, Alessandro, Settanni, Michele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957912/
https://www.ncbi.nlm.nih.gov/pubmed/35350734
http://dx.doi.org/10.3389/fpsyg.2022.830120
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author Marengo, Davide
Montag, Christian
Mignogna, Alessandro
Settanni, Michele
author_facet Marengo, Davide
Montag, Christian
Mignogna, Alessandro
Settanni, Michele
author_sort Marengo, Davide
collection PubMed
description More than three billion users are currently on one of Meta’s online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in more digital footprints to be studied and monetized (via psychological targeting). In this context, it is debated if social media platforms can elicit addictive behaviors. In the present work, we demonstrate in N = 1,094 users that it is possible to predict from digital footprints of the Facebook users their self-reported addictive tendencies toward social media (R > 0.30) by applying machine-learning strategies. More specifically, we analyzed the predictive power of a set of models based on different sets of features extracted from digital traces, namely posting activity, language use, and page Likes. To maximize the predictive power of the models, we used an ensemble of linear and non-linear prediction algorithms. This work showed also sufficient accuracy rates (AUC above 0.70) in distinguishing between disordered and non-disordered social media users. In sum, individual differences in tendencies toward “social networks use disorder” can be inferred from digital traces left on the social media platform Facebook. Please note that the present work is limited by its cross-sectional design.
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spelling pubmed-89579122022-03-28 Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach Marengo, Davide Montag, Christian Mignogna, Alessandro Settanni, Michele Front Psychol Psychology More than three billion users are currently on one of Meta’s online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in more digital footprints to be studied and monetized (via psychological targeting). In this context, it is debated if social media platforms can elicit addictive behaviors. In the present work, we demonstrate in N = 1,094 users that it is possible to predict from digital footprints of the Facebook users their self-reported addictive tendencies toward social media (R > 0.30) by applying machine-learning strategies. More specifically, we analyzed the predictive power of a set of models based on different sets of features extracted from digital traces, namely posting activity, language use, and page Likes. To maximize the predictive power of the models, we used an ensemble of linear and non-linear prediction algorithms. This work showed also sufficient accuracy rates (AUC above 0.70) in distinguishing between disordered and non-disordered social media users. In sum, individual differences in tendencies toward “social networks use disorder” can be inferred from digital traces left on the social media platform Facebook. Please note that the present work is limited by its cross-sectional design. Frontiers Media S.A. 2022-03-08 /pmc/articles/PMC8957912/ /pubmed/35350734 http://dx.doi.org/10.3389/fpsyg.2022.830120 Text en Copyright © 2022 Marengo, Montag, Mignogna and Settanni. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Marengo, Davide
Montag, Christian
Mignogna, Alessandro
Settanni, Michele
Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach
title Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach
title_full Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach
title_fullStr Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach
title_full_unstemmed Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach
title_short Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach
title_sort mining digital traces of facebook activity for the prediction of individual differences in tendencies toward social networks use disorder: a machine learning approach
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957912/
https://www.ncbi.nlm.nih.gov/pubmed/35350734
http://dx.doi.org/10.3389/fpsyg.2022.830120
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