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      Identification of central symptoms in problematic WeChat use and depression among Chinese college students: a network analysis

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          Abstract

          Background

          Previous research has identified a positive relationship between problematic social media use and depression. However, with the increasing diversification and personalization of social media platforms, the impact of different platforms on college students varies significantly. It is necessary to examine the specific symptom network of problematic use in certain platforms and its relationship with depression. Therefore, this study aims to address these gaps by investigating the symptom network of problematic WeChat use among college students and identifying bridge symptoms linking problematic WeChat use to depression.

          Method

          A total of 6,060 Chinese college students (Mean age = 18.32; range 16–21; 60.66% male, 39.34% female) were recruited and provided self-reports on their problematic WeChat use and depression levels. Latent profile analysis was employed to identify the profiles of problematic WeChat users. Subsequently, four network structures relating to problematic WeChat use and its association with depression were constructed among problematic users.

          Results

          The analysis revealed three heterogeneous groups of college students regarding their WeChat use, with 11.4% classified as problematic users. Network analysis indicated that the central symptoms of problematic WeChat use were “I feel the need to use WeChat with increasing amounts of time to achieve satisfaction” and “I have used WeChat to relieve of loneliness and stress”. No significant gender differences were found in the network structure of problematic WeChat use; the network strength was significantly higher among male students compared to female students. Within the combined network, the central symptoms included “self-dislike,” “pessimism,” “I feel happy and satisfied when I am on WeChat,” and “guilt.” A strong association was observed between problematic WeChat use and depressive symptoms.

          Conclusion

          The findings of this study further elucidate the impact of problematic WeChat use on depression among college students, providing both theoretical and practical insights for developing interventions targeting WeChat use and depression within this demographic.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12888-024-06235-8.

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          Most cited references46

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          Sparse inverse covariance estimation with the graphical lasso.

          We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
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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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              qgraph: Network Visualizations of Relationships in Psychometric Data

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                Author and article information

                Contributors
                chenke197729@163.com
                Journal
                BMC Psychiatry
                BMC Psychiatry
                BMC Psychiatry
                BioMed Central (London )
                1471-244X
                15 November 2024
                15 November 2024
                2024
                : 24
                : 814
                Affiliations
                Faculty of Social Sciences, Chongqing University, ( https://ror.org/023rhb549) Shapingba, Chongqing, 400044 China
                Article
                6235
                10.1186/s12888-024-06235-8
                11566438
                39548427
                2e5a320c-6864-4f31-bc4a-75731ae2bcfc
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 3 July 2024
                : 30 October 2024
                Funding
                Funded by: Fundamental Research Funds for the Central Universities
                Award ID: 2022CDJSKZX07
                Funded by: Project of Chongqing Municipal Programs for Social Science
                Award ID: 2021ZTZD10
                Funded by: Project of the Chongqing Municipal Committee of Science and Technology
                Award ID: cstc202ljsyj-zzysbAX0076
                Funded by: College Ideological and Political Work Cultivation Project of the Ministry of Education
                Award ID: ISZS2021-2
                Funded by: Project of the China Association of Higher Education
                Award ID: 2020FDD07
                Funded by: Projects of the Chongqing Municipal Education Commission of China
                Award ID: 19SKZDZX13 and 19SKSZ001
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Clinical Psychology & Psychiatry
                college students,problematic wechat use,depression,latent profile analysis,network analysis

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