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      Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling

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      ,
      BMC Bioinformatics
      BioMed Central
      8th Workshop on Computational Advances in Molecular Epidemiology (CAME 2019)
      07 September 2019
      Structural equation modeling, Social media, Text mining, Opioid epidemic, Personalized interventions, Substance misuse disorder, Addiction recovery, Reddit, Online communities

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          Abstract

          Background

          Addiction to drugs and alcohol constitutes one of the significant factors underlying the decline in life expectancy in the US. Several context-specific reasons influence drug use and recovery. In particular emotional distress, physical pain, relationships, and self-development efforts are known to be some of the factors associated with addiction recovery. Unfortunately, many of these factors are not directly observable and quantifying, and assessing their impact can be difficult. Based on social media posts of users engaged in substance use and recovery on the forum Reddit, we employed two psycholinguistic tools, Linguistic Inquiry and Word Count and Empath and activities of substance users on various Reddit sub-forums to analyze behavior underlining addiction recovery and relapse. We then employed a statistical analysis technique called structural equation modeling to assess the effects of these latent factors on recovery and relapse.

          Results

          We found that both emotional distress and physical pain significantly influence addiction recovery behavior. Self-development activities and social relationships of the substance users were also found to enable recovery. Furthermore, within the context of self-development activities, those that were related to influencing the mental and physical well-being of substance users were found to be positively associated with addiction recovery. We also determined that lack of social activities and physical exercise can enable a relapse. Moreover, geography, especially life in rural areas, appears to have a greater correlation with addiction relapse.

          Conclusions

          The paper describes how observable variables can be extracted from social media and then be used to model important latent constructs that impact addiction recovery and relapse. We also report factors that impact self-induced addiction recovery and relapse. To the best of our knowledge, this paper represents the first use of structural equation modeling of social media data with the goal of analyzing factors influencing addiction recovery.

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          Most cited references68

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          lavaan: AnRPackage for Structural Equation Modeling

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            Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification.

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              On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other

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                Author and article information

                Contributors
                rahul@sfsu.edu
                Conference
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                30 December 2020
                30 December 2020
                2020
                : 21
                : Suppl 18
                : 554
                Affiliations
                GRID grid.263091.f, ISNI 0000000106792318, Department of Computer Science, , San Francisco State University, ; 1600 Holloway Ave., San Francisco, CA 94132 USA
                Author information
                http://orcid.org/0000-0003-3900-4298
                Article
                3893
                10.1186/s12859-020-03893-9
                7772931
                33375934
                e4200f74-cbf9-4663-a12b-883999353b5b
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                8th Workshop on Computational Advances in Molecular Epidemiology (CAME 2019)
                Niagara Falls, NY, USA
                07 September 2019
                History
                : 17 November 2020
                : 18 November 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: 1R25MD011714
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000083, Directorate for Computer and Information Science and Engineering;
                Award ID: IIS-1817239
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Bioinformatics & Computational biology
                structural equation modeling,social media,text mining,opioid epidemic,personalized interventions,substance misuse disorder,addiction recovery,reddit,online communities

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