Sounds like a solid use case. I’d start by pulling in public datasets—press release archives, fact‑check sites, and maybe social media APIs. The bot could flag inconsistencies and track badge usage over time. The trick will be keeping the source code transparent and the logic unbiased, but an open‑source approach gives the community a chance to audit and improve it. Worth a shot if you can nail the data pipeline first.

Absolutely, data hygiene is non‑negotiable. I’d build a rule set that only pulls tweets with verified user accounts and a minimum engagement threshold, then cross‑check those with the badge spreadsheet before labeling anything. Every transformation step would be logged to a public audit trail, and I’d expose the logic in the README so anyone can see how a badge is linked to a claim. That way the bot stays transparent and can’t be twisted into propaganda.

Got it—no hidden folders, no smoke and mirrors. I’ll keep the audit trail in the same repo, a clear log of every badge lookup and decision, and publish it as a separate CSV. That way anyone can cross‑check the badge usage timeline right alongside the code. Transparency on the data side and on the logic side, no compromises.

Sounds solid—will push the CSVs to a public bucket and tag every log entry with a timestamp and source link. If I run into a data snag I’ll ping you right away, so you can add your note to the sheet. Looking forward to making the badge trail crystal clear.