I’m glad to see a new platform that isn’t completely locked down, allowing analysis like this.<p>The trend toward everything being a walled garden is unfortunate.
Meanwhile, over at Blueksy.app a few days ago, the users were incensed about a 1M-post data set and hounded the creator in to withdrawing it.<p><a href="https://bsky.app/profile/danielvanstrien.bsky.social/post/3lbu6l4fxdc2e" rel="nofollow">https://bsky.app/profile/danielvanstrien.bsky.social/post/3l...</a>
The paper is from the end of April and they say the data was collected in February, March and April. I guess we can talk about it now, though.<p>Due to high growth since then, this is from before most current users joined.
If you just want to play around with the data, check out the bsky dataset on Axiom <a href="https://play.axiom.co/axiom-play-qf1k/stream/bsky" rel="nofollow">https://play.axiom.co/axiom-play-qf1k/stream/bsky</a> (700M+ events and counting)
If you are interested in some real time visualization there are plenty of projects. For example <a href="http://www.graphtracks.com" rel="nofollow">http://www.graphtracks.com</a>. (I'm the author)
Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. To address this pressing issue, we present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social.<p>The dataset contains the complete post history of over 4M users (81% of all registered accounts), totaling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions.<p>Since Bluesky allows users to create and bookmark feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped “like” interactions and time of bookmarking.<p>This dataset allows unprecedented analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection, and performing content virality and diffusion analysis.