And check out the full paper in the open access @journalqd.bsky.social@antisomniac.bsky.social@ethanz.bsky.socialjournalqd.org/article/view...

Check out @antisomniac.bsky.social@idpiumass.bsky.socialpublicinfrastructure.org/2023/12/21/n...

Notes from Random YouTube Coding: 5 Main Takeaways - Initiative for Digital Public Infrastructure

We’re excited to announce the publication of our paper, Dialing for Videos: A Random Sample of YouTube, in the Journal of Quantitative Description: Digital Media. The article is the culmination of a...

5. There are an awful lot of video games Our hand-coding task found that nearly 30% of videos were about video games. Other topics represented a smaller part of our sample but were nonetheless surprising, like religious content, which made up about 3% of the videos we hand-coded!

4. Not everyone is participating in the “creator economy” Low-effort videos of still photos, homework assignments, inaudible three-second clips of ceremonies, and Zoom meeting recordings — YouTube's vast "dark matter" is more variable and strange than the YouTube we're used to.

3. Most of YouTube doesn’t get many views Odds are, the videos you watch have >1,000 views, but those videos were just 13% of our sample. 4.9% don’t have any views at all. Even more stark is when it comes to comments and likes — 72.6% have no comments and 88.7% have no likes.

2. YouTube is mostly not in English Our current best estimate is that 32% of videos where we can detect spoken language are in English, with 10.5% in Hindi, 8% in Spanish, slightly fewer in Portuguese, and just over 6% in Arabic.

1. YouTube hosts over 13 billion public videos (as of this month) Our sampling method allows us to estimate YouTube's total size and growth. This current estimate is up from our paper's year-old estimate of ~10 billion. Check out tubestats.org for our latest data!

TubeStats

TubeStats is an open-source tool that lets you navigate YouTube data and compare videos across the platform.

Our team's paper, "Dialing for Videos: A Random Sample of YouTube," is out now in @journalqd.bsky.social. We analyzed 10,000 random YouTube videos through metadata analysis, a spoken language identification model, and hand-coding. Here are our 5 main takeaways:

view from mt. tom in easthampton, ma. i love western mass!!

VERY excited to delve into: loud men talking loudly report on exclusionary cultures of internet governance – corinne cath. https://www.criticalinfralab.net/wp-content/uploads/2023/04/LoudMen-CorinneCath-CriticalInfraLab.pdf