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A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
Published on: May 10, 2022
Language, themes and peer interactions in reddit body dysmorphic disorder forums
Holly G Pedersen1, Ravi Iyer2, Philip J Sumner1
1School of Health Sciences, Swinburne University of Technology, Melbourne, Australia.
Abstract:
BDD involves intrusive appearance-related preoccupations and distress, yet many individuals remain outside clinical settings and may describe their experiences in anonymous online forums shaped by platform norms and peer audiences. This study presents the first large-scale, multimethod computational analysis of discourse and peer interaction in Body Dysmorphic Disorder (BDD) focused Reddit communities. We sought to characterise how content is organised within BDD-focused Reddit communities, including how users describe symptoms, express distress and engage with peers. We analysed 80,298 posts and comments from eight BDD focused subreddits posted between 2013 and 2025 by 10,614 unique users. Using a clinically informed symptom lexicon, we applied topic modelling, sentiment analysis and linguistic network measures to examine patterns in language use. Changes in discussion topics were tracked longitudinally at the user level and peer engagement was analysed using a directed reply network. Three discussion types emerged: suicidality/repetitive behaviours, affective/somatic distress and appearance control. These differed in emotional tone and language structure. Suicidality/repetitive behaviours related content showed lower lexical diversity and less connected language than other content, suggesting more fragmented expression during severe distress. Most posts/comments were questions (48.1%), venting (31.7%) or advice-related (10.2%). The user reply network was sparse (density = 0.0003), reflecting many one-off interactions, but 51.1% of replies crossed discussion types, indicating that users often engaged with others experiencing different forms of distress. BDD discussions on Reddit show clear patterns in how distress is expressed and how users interact. These findings may complement clinical research and help inform context-sensitive digital mental health support.
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