A NOVEL THEORY OF SUPPORT IN SOCIAL MEDIA DISCOURSE (WITH ARTIFICIAL INTELLIGENCE AND LINGUISTICS ANALYSIS)
type: CreativeWork
description: This thesis 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. It explores their utilisation of novel salient high-frequency and diverse device-enabled discourse categories of purpose and content. The predominate analysis is from computing and artificial intelligence. It is with an aspiration for the novel methods and novel theory to find its place in corpus linguistics—subtle linguistic analysis guides the exploratory research. An investigation of sophisticated patterns of support alludes to an architecture for social media discourse. The primary patterns can include device-enabled discourse categories of purpose and content. My thesis calls this vose and nent. Advice with stance-taking offers an entry into the large-scale analysis. It forms part of a multitude of online topics and discourses from diabetes advice to raising charity money. The thesis focuses mainly on domain-specific targets of diabetes, blood, and pumps from many others ranging from targets of usernames, child, school, greetings, places, time, to years. The thesis proposes a novel theory of support constructs in social media discourse. The thesis makes a methodological contribution that seeks to combine Artificial Intelligence (AI) computational analyses with corpus linguistics (CL) and qualitative SFL analysis. It is carried out on a big large-scale dataset of 218,068, anonymised Facebook Diabetes UK posts and 16,137 anonymous diabetes-related users of the platform. AI with Latent Dirichlet Allocation (LDA) topic-modelling probabilistic modelling, automated content analysis, an annotation for entity recognition and Discourse Analysis (DA) is utilised with consideration of its limitations but to analyse the corpora for potential patterns. An adapted anonymisation process is used on the data to meet the ongoing challenges of online ethical research requirements. People living with diabetes employ high-frequen…
abstract: This thesis 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. It explores their utilisation of novel salient high-frequency and diverse device-enabled discourse categories of purpose and content. The predominate analysis is from computing and artificial intelligence. It is with an aspiration for the novel methods and novel theory to find its place in corpus linguistics—subtle linguistic analysis guides the exploratory research. An investigation of sophisticated patterns of support alludes to an architecture for social media discourse. The primary patterns can include device-enabled discourse categories of purpose and content. My thesis calls this vose and nent. Advice with stance-taking offers an entry into the large-scale analysis. It forms part of a multitude of online topics and discourses from diabetes advice to raising charity money. The thesis focuses mainly on domain-specific targets of diabetes, blood, and pumps from many others ranging from targets of usernames, child, school, greetings, places, time, to years. The thesis proposes a novel theory of support constructs in social media discourse. The thesis makes a methodological contribution that seeks to combine Artificial Intelligence (AI) computational analyses with corpus linguistics (CL) and qualitative SFL analysis. It is carried out on a big large-scale dataset of 218,068, anonymised Facebook Diabetes UK posts and 16,137 anonymous diabetes-related users of the platform. AI with Latent Dirichlet Allocation (LDA) topic-modelling probabilistic modelling, automated content analysis, an annotation for entity recognition and Discourse Analysis (DA) is utilised with consideration of its limitations but to analyse the corpora for potential patterns. An adapted anonymisation process is used on the data to meet the ongoing challenges of online ethical research requirements. People living with diabetes employ high-frequen…
author: Solomon, Bazil Stanley
language: en
date published: 2020
date modified: 2026-07-24T03:43:44Z
provider: Oxford Brookes University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Oxford Brookes University

PropertyValue
property ID: DOI
value: 10.24384/4xky-qv73