A machine learning librarian at Hugging Face just released a dataset composed of one million Bluesky posts, complete with when they were posted and who posted them, intended for machine learning research.
Daniel van Strien posted about the dataset on Bluesky on Tuesday:
“This dataset contains 1 million public posts collected from Bluesky Social’s firehose API, intended for machine learning research and experimentation with social media data,” the dataset description says. “Each post contains text content, metadata, and information about media attachments and reply relationships.”
The data isn’t anonymous. In the dataset, each post is listed alongside the users’ decentralized identifier, or DID; van Strien also made a search tool for finding users based on their DID and published it on Hugging Face. A quick skim through the first few hundred of the million posts shows people doing normal types of Bluesky posting—arguing about politics, talking about concerts, saying stuff like “The cat is gay” and “When’s the last time yall had Boston baked beans?”—but the dataset has also swept up a lot of adult content, too.
Plenty of things are more difficult in decentralized systems.
You have to store all kinds of data either in multiple copies/caches or get long delays on certain operations such as search or even just displaying aggregated data (such as a post and its comments from different instances on Lemmy).
You have the problem of different jurisdictions and moderation policies for different instances.
You will have a hard time exporting or deleting all data related to a specific user when required by law (e.g. GDPR).
Difficult != Can’t be done. I’m well aware of the difficulties. Distributed systems design is one of my specialties.
GDPR only applies to your servers. Data deletion is probably the easiest part to deal with.