ADHD Discourses on Social Media

This chapter provides a systematic account of the methodological framework, data collection procedures, analytical pipeline, and associated limitations governing the first phase of empirical work for this research project. It is intended both as an audit trail for the researcher's own subsequent work and as a methodological statement suitable for incorporation into formal academic outputs.

The project adopts an inductive, interpretivist epistemological position. Rather than proceeding from fixed hypotheses or predetermined analytical categories, the research allows themes, discourses, and patterns to emerge from sustained engagement with the data. This positions the work within critical qualitative and discourse-analytic traditions, drawing particularly on Foucauldian approaches to knowledge, identity, and the production of truth.

The methodological choices documented below — including the selection of data source, sampling strategy, and analytical framework — were made in deliberate alignment with this epistemological commitment. The use of AI as an analytical collaborator is not incidental to this approach; it is theorised as a productive extension of interpretive method, not a replacement for it.

The analytical method employed is best characterised as AI-Assisted Inductive Discourse Analysis (AIDA). This is an emergent methodological approach that combines the interpretive and critical traditions of discourse analysis with the large-scale pattern recognition capabilities of Large Language Models — specifically Claude (Anthropic).

Discourse analysis, in its foundational sense, is concerned not merely with what is said but with how language constructs social realities, identities, and relations of power. AIDA extends this tradition by using AI to process data at a scale beyond what is feasible through human analysis alone, while preserving the interpretive, critical commitments of the qualitative tradition through researcher oversight, reflexivity, and iterative validation.

The primary data source is Reddit — specifically the subreddits r/ADHD, r/ADHDwomen, r/ADHD_partners, and r/adhdmemes. Reddit was selected because it represents one of the largest and most active English-language communities of self-identified neurodivergent people, with documented research utility as a source of naturalistic, unsolicited first-person accounts of ADHD experience.

Data was collected using the Pushshift API (via academic access) and Reddit's own public API. A corpus of approximately 20,000 posts was assembled through a combination of keyword-based sampling (using ADHD-specific terminology, diagnostic language, and community-specific vocabulary) and temporal sampling (posts from January 2020 to December 2025, capturing the COVID-19 pandemic period and the subsequent surge in ADHD diagnoses).

Sampling Strategy

A stratified purposive sampling approach was applied to ensure representation across time periods, subreddit communities, post types, and engagement levels. High-engagement posts (above the 90th percentile for comments) were oversampled to capture posts that generated community discourse rather than individual expression only. All personally identifiable information was anonymised prior to analysis.

The analysis proceeded through six stages, each documented and auditable:

The ADHD Discourse Analyser — an interactive web tool built to support this research — implements stages 1–4 of this pipeline and is available as part of the AI Projects portfolio.