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      Identifying the central symptoms of problematic social networking sites use through network analysis

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          Abstract

          Background

          Problematic social media use (PSMU) has received growing attention in the last fifteen years. Even though PSMU has been extensively studied, its internal structure is not fully understood. We used network analysis to evaluate which symptoms and associations between symptoms are most central to PSMU – as assessed by the Generalized Problematic Internet Use Scale-2 adapted for PSMU – among undergraduates.

          Method

          Network analysis was applied to a large gender-balanced sample of undergraduates ( n = 1344 participants; M = 51.9%; mean age = 22.50 ± 2.20 years).

          Results

          The most central nodes in the network were the difficulty of controlling one’s own use of social media, the tendency to think obsessively about going online, the difficulties in resisting the urge to use social media and the preference for communicating with people online rather than face-to-face. This last element was strongly associated with a general preference for online social interactions and the feeling of being more comfortable online. The network was robust to stability and accuracy tests. The mean levels of symptoms and symptom centrality were not associated.

          Conclusions

          Deficient self-regulation and preference for online communication were the most central symptoms of PSMU, suggesting that these symptoms should be prioritized in theoretical models of PSMU and could also serve as important treatment targets for PSMU interventions.

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          Most cited references68

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          Estimating psychological networks and their accuracy: A tutorial paper

          The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online. Electronic supplementary material The online version of this article (doi:10.3758/s13428-017-0862-1) contains supplementary material, which is available to authorized users.
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            A cognitive-behavioral model of pathological Internet use

            R.A. Davis (2001)
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              Network analysis: an integrative approach to the structure of psychopathology.

              In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry → insomnia → fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therapeutic interventions.
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                Author and article information

                Contributors
                Journal
                J Behav Addict
                J Behav Addict
                JBA
                Journal of Behavioral Addictions
                Akadémiai Kiadó (Budapest )
                2062-5871
                2063-5303
                25 August 2021
                October 2021
                October 2021
                : 10
                : 3
                : 767-778
                Affiliations
                [1] Department of Health Sciences, Section of Psychology, University of Florence , Italy
                Author notes
                [* ]Corresponding author. Email silvia.casale@ 123456unifi.it
                Author information
                https://orcid.org/0000-0001-5183-6113
                https://orcid.org/0000-0003-4871-2755
                https://orcid.org/0000-0003-3823-9773
                Article
                10.1556/2006.2021.00053
                8997205
                34437299
                f4f056c7-0d9f-42bb-a110-814694c7ee6b
                © 2021 The Author(s)

                Open Access. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated.

                History
                : 19 January 2021
                : 29 April 2021
                : 12 July 2021
                : 25 July 2021
                Page count
                Figures: 5, Tables: 2, Equations: 0, References: 69, Pages: 12
                Categories
                Article

                behavioral addictions,technological addictions,generalized problematic internet use scale-2,problematic social media use,problematic social networking sites use,network analysis

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