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BLIUC, Ana-Maria, IQBAL, Muhammad and BEST, David (2019). Integrating computerized linguistic and social network analyses to capture addiction recovery capital in an online community. Journal of visualized experiments : JoVE (147).
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Abstract
The article describes a new methodology designed with the aim of finding a comprehensive, unobtrusive, and accurate way of capturing social recovery capital development in online communities of recovery from alcohol and drug (AOD) addiction. Recovery capital was conceptualised as both engagement in the online recovery community and identification with the community. To measure recovery capital development, naturally occurring data were extracted from the social media page of a specific recovery program, with the page being set up as a resource for a face-to-face recovery program. To map engagement with the online community, social network analysis (SNA) capturing online social interaction was performed. Social interaction was measured through the linkages between the online contributors/members of the online community as represented by program clients, staff, and supporters from the broader community. To capture markers of social identification with the online community, computerised linguistic analysis of the textual data (content from posts and comments) was conducted. Recovery capital captured in this way was analysed against retention data (a proxy outcome indicator), as days spent in the (face-to-face) recovery program. The online data extracted was linked to participant data in regards to program retention to test prediction of a key recovery outcome. This approach allowed the examination of the role of online support communities and assessment of the association between recovery capital (developed via the online community of recovery) and recovery outcomes.
| Item Type: | Article |
|---|---|
| Identification Number: | https://doi.org/10.3791/58851 |
| SWORD Depositor: | Symplectic Elements |
| Depositing User: | Symplectic Elements |
| Date Deposited: | 24 Jul 2019 11:09 |
| Last Modified: | 18 Mar 2021 03:46 |
| URI: | https://shura.shu.ac.uk/id/eprint/24903 |
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