I'm a computer science professor at Princeton. I write about AI hype & harms, tech platforms, algorithmic bias, and the surveillance economy.
I've been studying decentralized social media since the late 2000s, so I'm excited to use and write about Mastodon at the same time.
Check out this symposium on algorithmic amplification that I'm co-organizing: https://knightcolumbia.org/events/optimizing-for-what-algorithmic-amplification-and-society
| Website | https://www.cs.princeton.edu/~arvindn/ |
| Substack: AI Snake Oil | https://aisnakeoil.com/ |
| Book: Fairness and machine learning | https://fairmlbook.org/ |
Here's a nice article explaining why there are many ways of measuring the size of Mastodon and why there's no single right way to measure it. https://absolutelymaybe.plos.org/2022/12/05/mastodon-growth-numbers-might-not-mean-what-you-think-they-mean/
The main reason why the total user count of 6 million in my chart is different from the 9 million often reported by sources such as @mastodonusercount is that the latter includes the part of Mastodon that is blocked by most instances for hate speech and other such content.
The number of active Mastodon users — those who've logged in in the last 30 days — has fallen to 1.8 million from a high of 2.6 million. This reflects the fact that while there's a wave of new users after each Musk tantrum, the majority aren't sticking around. Even the 1.8M figure is inflated by new accounts, so I expect a further drop.
Still, the number of active users is over 4 times what it used to be before the Twitter takeover.
Source: https://api.joinmastodon.org/statistics (and Wayback machine).
Here's a fascinating chart showing Mastodon adoption among a few scientific communities by @TrueSciPhi. https://mastodon.truesciphi.org/@TrueSciPhi/109554915418774658
I'd love to see charts like this for other communities.
One thing the chart shows is that Dear Leader's recent decree about Mastodon had a small but measurable effect: 5-10% of those who had their Mastodon info in their bios removed it.
Attached: 1 image Mastodon lists based on Twitter lists: Astronomers: https://truesciphi.org/ast_mas.html Physicists: https://truesciphi.org/phy_mas.html Professional Science Writers: https://truesciphi.org/psw_mas.html Mathematicians: https://truesciphi.org/mat_mas.html Philosophers: https://truesciphi.org/phi_mas.html
This is an amazing chart https://sigmoid.social/@hal/109389647341556477
It shows that the Lake Wobegon effect shows up even in a population that's presumably fluent in statistics (NeurIPS authors). When submitting papers, authors wildly overestimate their probability of acceptance — the median prediction was 70% — even after being told that the base rate in prior years was 21%.
This is the opposite of the Dunning-Kruger effect: those with *lower* acceptance rates are well calibrated!
Attached: 1 image New blog post on the NeurIPS'21 experiment re authors' perceptions of their own papers! https://blog.ml.cmu.edu/2022/11/22/neurips2021-author-perception-experiment/ Key findings: 1) Authors significantly overestimate their papers' chances of acceptance. By like a LOT. >
Princeton is a gorgeous small town to live in, but many of us also live in NYC, Philadelphia, or other nearby areas. I'd be happy to answer any questions about any of our positions.
Here's our website: https://citp.princeton.edu/
