- false positives
- false negatives
- brigading
- flagged topics vs off-HN contexts
- flagged topic trends over extended time
- content origin (domain, geo, org, author)
> The open data would tell an incomplete picture. It would still be an interesting to see what you can pull out, but you'd be guessing why a lot of the time.
The current implementation is like the DMCA. Anything can be censored for any reason without input from HN moderators. If the submitter is aware of the process, they can appeal to HN moderators. If HN readers saw the submission before it was flagged, they can also appeal to HN moderators.
If there were organized efforts to censor specific topics, one mitigation could be to deny the benefits of censorship. This could be done from public data by extracting a queue of flagged submissions and feeding them into another level of non-public analysis, automated + human. The surviving stories could then be posted to a dedicated site, e.g. using https://github.com/lobsters/lobsters.
If there were organized efforts to censor specific topics, a secondary public log of valid-but-censored articles could trigger the Streisand effect. Not only could this deny the benefit of censorship, it could cause censorship to have the opposite effect, reducing demand for organized flagging. This would have no impact on organic, non-coordinated flagging.
Add to the facets: the submitted domain. Maybe posts by the armchair experts and quarterbacks on Substack get flagged more often than the WSJ, for example.