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dc.contributor.authorKoschate-Reis, M
dc.contributor.authorNaserian, E
dc.date.accessioned2021-02-17T13:01:22Z
dc.date.issued2021-04-30
dc.description.abstractThe Social Identity Model of Recovery (SIMOR) suggests that addiction recovery is a journey through time where membership in various groups facilitates success. With the help of computational approaches, we now have access to new resources to study whether a wide variety of different online communities can be part of the addiction recovery journey. In this work, we study the effects of two main social factors on recovery success: first, multiple group membership defined in terms of richness of online community engagement;second, active participation operationalized as the evenness in engagement with these groups. We then model recovery from addiction by applying the extended Cox regression model which accounts for the effect of these two factors on time to relapse. We applied our analysis to a dataset of 457 recovering opioid addicts that self-announced the date of their recovery, indicating that at least 219 (48%) addicts relapsed during the recovery period. We find that multiple group membership – in terms of the number of other forums that a subject had posted in - as well as active participation - in terms of how evenly their posts were spread amongst the different forums - reduced the risk of relapse. We discuss our findings with regards to the opportunity, but also risk, that online group membership poses for recovering opioid addicts, as well as the possible contribution that computational social science methods can make to the study of addiction and recovery.en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.identifier.citationVol. 5 , Issue CSCW1, article 68en_GB
dc.identifier.doi10.1145/3449142
dc.identifier.grantnumberEP/S001409/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/124777
dc.language.isoenen_GB
dc.publisherAssociation for Computing Machinery (ACM)en_GB
dc.rights© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.en_GB
dc.subjectaddiction recoveryen_GB
dc.subjectsocial cureen_GB
dc.subjectonline communitiesen_GB
dc.subjectsurvival analysisen_GB
dc.titleDo Group Memberships Online Protect Addicts in Recovery against Relapse? Testing the Social Identity Model of Recovery in the Online Worlden_GB
dc.typeArticleen_GB
dc.date.available2021-02-17T13:01:22Z
dc.identifier.issn2573-0142
dc.descriptionThis is the author accepted manuscript. The final version is available from ACM via the DOI in this recorden_GB
dc.descriptionCSCW 2021: 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, 23 - 27 October 2021. Onlineen_GB
dc.identifier.journalProceedings of the ACM on Human-Computer Interactionen_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
pubs.funder-ackownledgementYesen_GB
exeter.funder::Engineering and Physical Sciences Research Council (EPSRC)en_GB
rioxxterms.versionAMen_GB
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-02-16T17:54:36Z
refterms.versionFCDAM
refterms.dateFOA2021-05-07T14:09:19Z
refterms.panelAen_GB


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