A multi-platform computational study comparing public discourse on AI wearables across Instagram, Reddit, and academic literature — examining how privacy concerns surface across 100k+ data points.
01 — Project Overview
Smart glasses have re-emerged after the Google Glass era with far greater commercial success — yet the core privacy tensions remain. This project asked: how are people actually talking about AI smart glasses privacy concerns, and are those conversations reaching the institutions best positioned to act on them?
We analysed Instagram Reels comments, Reddit posts, and peer-reviewed academic literature using a computational mixed-methods approach, then compared findings across all three to surface gaps between public concern, platform discourse, and scholarly attention.
Coordinated a 4-person cross-functional team across data collection, NLP pipelines, and write-up phases. Managed timeline, task distribution, and milestone check-ins.
Analysed Academic and Social Media Data, data cleaning, sentiment categorization fine-tuning, contributed to DistilBERT classification of academic literature.
02 — Development Process
Select any step to expand the details — including specific contributions, decisions made, and tradeoffs involved.
As the project's de-facto business analyst and project manager, I facilitated early team workshops to align on what we were actually trying to answer.
Before touching any data, I led a structured review of prior work on smart glasses, sentiment analysis methodologies, and platform audience research.
Each platform required a distinct scraping strategy. I coordinated all three parallel tracks and enforced quality standards throughout.
A key decision was using different models per platform rather than a single universal classifier.
I designed and maintained the thematic classification layer using regex-based keyword maps to capture what was actually being discussed.
I led the analytical synthesis phase — identifying patterns that no single platform's data could reveal on its own.
Distilled the research into a coherent academic narrative and authored an ethical analysis appendix beyond the original brief.
03 — Key Findings
Select a platform to explore the findings. Each tells a different story about how privacy concern surfaces — or gets buried.
Instagram comments were dominated by hype language. Top terms included love, amazing, fire, funny, and great. Buyer intent rose steadily from 2023–2026, framing the glasses increasingly as a fashion purchase.
Key Insight: Even after a February 2026 exposé revealing Meta glasses had been covertly recording users, there was no measurable change in sentiment or privacy-mention rates. This suggests a privacy indifference pattern on algorithm-curated consumer platforms — concern spikes briefly, then subsides, and buying intent continues upward.
Reddit showed a bifurcated landscape: enthusiast communities like r/RaybanMeta were net positive, while r/privacy and r/law were overwhelmingly negative. Negative proportion rose from ~20% in 2013 to over 28% in 2025 — skepticism grew alongside adoption.
Key Insight: Negative posts generated the highest median comment counts — privacy concerns, while a minority of posts, sparked the most sustained discussion. Reddit's community structure creates the impression of a divided public; in reality it is non-communicating groups talking within their own priors.
The dominant academic concern was biometric surveillance — facial recognition, gaze tracking, iris scanning. Broader social harms like bystander discomfort and normalisation of ambient surveillance were far less represented.
Unexpected Finding: The most surprising result was how muted the academic response was. For a technology with clear, documented privacy harms, only 26 of 336 relevant papers took a clearly critical stance — showing the assumption of institutional rigour is not automatic.
Placing all three sources side-by-side reveals a consistent pattern: privacy concern exists across all three spaces, but never reaches critical mass in any of them. Instagram buries it in consumer enthusiasm. Reddit concentrates it in echo chambers. Academia describes rather than challenges it.
Synthesis: The data shows that the spaces best positioned to push back are either too siloed or too neutral to create the broad pressure needed for change. The fact that negative content drives disproportionate engagement on both platforms hints at a population that recognises the concern but does not feel it is appropriate to voice in consumer-coded spaces.
04 — Skills Demonstrated
This project combined technical execution, research leadership, and strategic communication — spanning roles typically distributed across multiple team members.
Key Takeaways
The strength came from combining automated pipelines with mandatory manual validation at every stage — a lesson directly applicable to product research and user testing design.
Instagram's algorithm and Reddit's community structure shape which concerns become visible and which never travel between spaces. Designing for feedback and privacy disclosure must account for these structural realities.
When negative content drives disproportionate engagement despite being a minority, the raw sentiment distribution may underrepresent actual user concern — a key insight for any UX researcher interpreting social data.
Academia proved as muted as the social platforms — showing that assumptions of institutional rigour are not automatic, and that meaningful research questions often emerge from looking where others aren't.