Journal of Diabetology

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A novel theory of support constructs in social media discourse (SSMD) through a study of an online Facebook Diabetes community

27th International Conference on Diabetes and Endocrinology
May 16-17, 2019 | Prague, Czech Republic

Bazil Stanley Solomon, Nigel Crook, Alon Lischinsky and Kenneth Boness

Oxford Brookes University, United Kingdom

Posters & Accepted Abstracts : J Diabetol


This study aims to inform the way that people are directly affected by various issues and conditions, and how they can support each other on social media by exploring their utilization of salient advice with stance-taking linguistic features for chronic illness support. The study develops a novel theory of support constructs in social media discourse. The study makes a methodological contribution that seeks to combine corpus linguistics (CL) with Artificial Intelligence (AI) computational analyses and qualitative linguistic discourse analysis to a large-scale dataset of over 200,000, anonymized Facebook Diabetes UK posts and 16,137 anonymous diabetesrelated users of the platform. An adapted anonymization process is used on the data to meet the ongoing challenges of online ethical research requirements. People living with diabetes are found to employ patterns of ‘topics’ and advice, with stance-taking in their support of themselves and each other. They tend to support each other during chronic illness with a language pattern that includes purpose, context and content discourse devices. These are in a broader context of power and solidarity, demonstrating social relations concerning risk and trust. Hence, the uncertainty and variation of effect displayed when sharing information for support. Loglikelihood, precision measures and a multi-method approach help to confirm the trends.

The implications of the new theory are aimed at healthcare communicators to work with organizations to help their social media users support each other by understanding a peerfocused view of chronic illness support. Corpus linguistics may benefit from the use of combined AI and DA approaches to anonymized large-scale online data. This study also offers preliminary work for support-bots to be programmed to utilize the language patterns to automatically support people who need them. The bots may be able to have conversations instantaneously with many people, but to do so in natural ways.



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