๐Ÿ“– Digital Ethnography ยท medRxiv Preprint ยท 2026

Breaking the Silence
in the Digital Village

A PCMCI causal analysis of Tamil community mental health discourse on TikTok โ€” centering @thadamofficial

Mithun Manivannan, BSc โ€” Carleton University, Ottawa, Canada

390

TikTok Videos

45

Months

10

Themes

8

Causal Links

โˆ’0.58

Strongest MCI

๐ŸŒฑ

This research demo is built on top of 29k/Aware โ€” the free, open-source mental health app by the non-profit 29k Foundation. UI, design system, and components are used under AGPL-3.0. We are deeply grateful to the 29k team for building open, community-first mental health infrastructure.

Visit 29k.org โ†’

Key Findings

What the data shows

8 significant causal links discovered via PCMCI (Tigramite, FDR q < 0.05, 500-permutation null model). The headline finding changes how we understand community mental health content strategy.

โˆ’0.58

Self-Harm & Suicide โ†’ Stigma (ฯ„=2 months)
Direct crisis content suppresses stigma discourse two months later. Computational proof of the "trust-then-challenge" arc.

+0.33

Self-Harm & Suicide โ†’ Help-Seeking (ฯ„=1 month)
Crisis content precedes help-seeking content โ€” the niederschwelliges (low-threshold) pathway computationally confirmed.

Explore the Research

Interactive research companion

Explore all 390 videos, the live causal graph, and the 45-month theme timeline โ€” each directly linked to @thadamofficial's TikTok content.

SOTA Pipeline (v3)

5-module causal inference pipeline

[A]TF-IDF + TextRank
[B]8-type DiGraph
[C]21-channel Matrix
[D]ADF โ†’ Granger โ†’ PCMCI
[E]Graph Diffusion + Narrative Roles