Dataset Building
Collect once, then refresh the whole table in a handful of calls.
A dataset is two jobs wearing one name. Collecting it is a search problem: find everything matching your criteria and keep the ids. Maintaining it is a throughput problem: read those same ids again next week without making one request per row. The batch endpoints take up to 100 ids at a time, which turns a ten-thousand-row refresh into a hundred scheduled calls rather than a queue somebody has to operate.
Build it into a product.
Collect matching posts once, then re-read up to 100 stored ids per call whenever the dataset needs refreshing.
Supported responses use a consistent ActivityStreams 2.0-based model. Shared concepts map to consistent fields, while platform-specific capabilities remain documented. Standard calls use one credit; batch endpoints charge one credit per submitted ID. Failed and timed-out requests are automatically refunded.
What this covers.
Public data only. Supported endpoints return what the platform serves publicly, and reaching it never depends on a connected account — so private analytics for accounts you own are outside what this can answer.
- Build a corpus from a defined set of communities and keep it current
- Refresh scores and comment counts on stored posts without a request per row
- Re-read a fixed id list on a cadence so the series stays comparable
- Collect the comment threads for a whole post set in batches
Try it with your own accounts.
Get a free API key and make the first call. 100 credits, every platform, both interfaces, no credit card.