ORCID

0000-0002-1111-2898

Keywords

Chronic Pain, Online Health Communities, Digital Health, Online Support, Nonlinear Dynamics, Peer Support

Abstract

Chronic pain is a highly prevalent medical condition that contributes to intense emotional distress and social isolation in patients. With the growth of social media, online health communities (OHCs) have become a popular resource for patients to learn about their pain and establish social connections with peers. Although there is some evidence suggesting that curated online resources can benefit patient well-being, social media interactions have the potential to propagate medical misinformation and negativity. This dissertation expands upon the Social Communication Model of Pain to examine how interactions within social media-based OHCs impact chronic pain and uncover practical insights into OHC efficacy. The impact of OHC participation on language, well-being, and pain-related distress is investigated across three separate studies, combining methods from natural language processing and nonlinear dynamical modeling. In studies 1 and 2, cross-wavelet analysis and recurrence quantification are used to model changes in language affect, style, and synchrony using observational data from chronic pain OHCs on Reddit. In study 3, patient reported outcomes are compared to perceptions of social support in OHCs. In addition, qualitative analysis is used to examine how participants believe OHCs could be enhanced to improve their user experience. Beyond improving our understanding of the health impacts of social media-based OHCs, this work introduces several dynamical approaches for analyzing online social interactions. It is among the first to apply bivariate wavelet analysis to explore affective synchrony in social media, providing a multiresolution view of the mechanisms underlying social influence. Additionally, a novel extension to cross-recurrence quantification analysis is introduced to enable dynamical descriptions of language style matching. The methodological, theoretical, and practical implications of our findings are discussed.

Completion Date

2025

Semester

Spring

Committee Chair

Jean, Mary

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

School of Modeling, Simulation, and Training

Identifier

DP0029357

Document Type

Dissertation/Thesis

Campus Location

Orlando (Main) Campus

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