Structuring Social Data for AI

This case study details the pipeline of extracting, structuring, and validating public sentiment data surrounding privacy and safety concerns related to AI-powered wearable devices. Discussions around Meta Ray-Ban smart glasses intensified in early 2026 as users, researchers, and online communities debated issues such as surveillance, consent, data collection, and transparency in AI training practices.

To analyze this broader conversation, the project integrated Vivly to autonomously identify signals and Aquin to rigorously inspect the dataset. While the retrieval pipeline surfaced a much larger corpus of wearable AI and privacy-related discussions, this case study specifically focuses on conversations related to Meta Ray-Ban smart glasses. The final result is a curated 1,500-entry training dataset sourced from Reddit and Hacker News that captures public discussions around consumer trust, privacy expectations, passive recording concerns, and AI-enabled wearable technology.

The dataset was collected during a period of heightened public discussion around privacy practices associated with Meta Ray-Ban glasses. Conversations across online communities focused on topics such as passive recording, AI training transparency, third-party data handling, and how wearable AI devices may affect expectations of privacy in public and personal spaces.

One incident that intensified these discussions involved investigative reports alleging that contractors associated with AI data labeling operations reviewed user-generated video and audio clips captured through the glasses. The reports raised broader concerns around informed consent, transparency in AI training workflows, data handling practices, and the privacy implications of wearable AI systems.

Following public backlash and regulatory scrutiny, Meta reportedly paused and later ended parts of its collaboration with Sama, a Kenya-based data annotation company involved in the workflow. The controversy also contributed to public debate around AI governance, consumer trust, and responsible deployment of always-on wearable technologies.

This project utilizes two primary platforms to process the unstructured data into a secure training set.

4.1 Data Sources

To capture authentic public reactions to Meta Ray-Ban smart glasses, we sourced data from two primary platforms: Reddit and Hacker News. These sites host some of the most unfiltered debates on emerging tech and privacy.

Reddit

Hacker News

We used the Vivly CLI to automate the extraction.

$ vivly route \
    "Meta Ray-Ban wearable AI privacy discussions and recording concerns" \
    --reddit --hackernews \
    --items=1500 --format=jsonl

We queried the Vivly SDK with the above prompt, and it analysed the intent and identified the specific communities actively discussing it.

r/RayBanStories

r/RaybanMeta

r/privacy

Using the Vivly SDK, the pipeline initially surfaced several thousand public discussions across Reddit and Hacker News related to wearable AI, privacy expectations, surveillance concerns, and Meta Ray-Ban smart glasses. After multiple filtering and validation stages designed to remove noise, duplicates, and low-relevance entries, the dataset was refined into a curated corpus of 1,500 high-signal discussions for downstream analysis and experimentation.

{
  "id": "1stq3ct",
  "url": "https://www.reddit.com/r/privacy/comments/1stq3ct/...",
  "score": 479,
  "title": "Being recorded with meta glasses during work",
  "content": "Today I was doing my job at a restaurant...",
  "subreddit": "privacy",
  "created_date": "2026-04-23T17:51:33+00:00",
  "num_comments": 227,
  "comments": [
    {
      "id": "ohv9lnq",
      "body": "Mention it to bosses as it has to be addressed in some standard
               yet inoffensive way for staff - that you can politely decline to
               be recorded more than a couple of seconds, say.",
      "score": 351,
      "depth": 0,
      "created_utc": 1776968911.0
    }
  ]
}

5.1 Dataset Preparation for Aquin

The raw JSON data extracted from Reddit and Hacker News was deeply valuable but far too unstructured for direct model training

The model analyzed the raw data and logically grouped scattered discussions based on shared article links and core topics. This preserved the contextual richness of the human conversations while organizing them into coherent, unified threads.

This consolidation step compressed approximately 1,500 individual discussions into 296 structured conversational rows while preserving the core semantic context and sentiment patterns present across the source material. The resulting structure was significantly more efficient for downstream inspection, clustering, and compliance analysis.

Once the discussions were logically grouped, the data was passed through a lightweight formatting script. This step required no additional AI processing. The script converted the grouped data into a strict, LLaMA-compatible prompt-and-answer format. The output was a clean JSON Lines (JSONL) file structured to match the ingestion requirements of Aquin's Dataset Inspector.

Finally, the formatted JSONL file was uploaded into Aquin, where the Dataset Inspector automatically processed the entries through its predefined evaluation pipelines.

A selection of views from the dataset inspector, audit surfaces, and pipeline output across each stage of the project.

The challenge with social data has rarely been access. The real difficulty is turning noisy, inconsistent public discussions into datasets that are structured enough for downstream analysis and model development. Public forums contain sarcasm, reposts, fragmented context, and low-signal commentary that make reliable dataset construction difficult at scale.

This project explored a different approach. Using the Vivly SDK, the pipeline identified relevant communities discussing wearable AI privacy concerns and surfaced high-signal discussions related to Meta Ray-Ban smart glasses. After filtering and refinement, the dataset was narrowed into a curated set of 1,500 discussion entries aligned with the core themes of the case study.

The structured output was then passed through a Claude-assisted organization stage followed by Aquin's inspection pipeline. Across multiple automated inspection layers, the dataset showed strong structural consistency with minimal indicators of adversarial manipulation, synthetic amplification, or poisoned content.

Because the dataset was collected directly from public online forums, certain discussions still contained personally identifiable information and sensitive user-provided details originating from the source platforms themselves. Before any downstream training or experimentation, the next stage of the pipeline will focus on PII scrubbing, remediation, and compliance alignment to ensure the dataset is safer and more suitable for research use.