Estimating the size of YouTube

YouTube doesn’t offer numbers for how big they are, so Ethan Zuckerman and Jason Baumgartner estimated the size using a method they equate to drunk dialing.

Consider drunk dialing again. Let’s assume you only dial numbers in the 413 area code: 413-000-0000 through 413-999-9999. That’s 10,000,000 possible numbers. If one in 100 phone calls connect, you can estimate that 100,000 people have numbers in the 413 area code. In our case, our drunk dials tried roughly 32k numbers at the same time, and we got a “hit” every 50,000 times or so. Our current estimate for the size of YouTube is 13.325 billion videos – we are now updating this number every few weeks at tubestats.org.

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Rankings for YouTube video greetings

If you’ve watched even just a few videos on YouTube, you probably noticed that many videos, especially those in the vlogging genre, start the same way: “Hey guys.” YouTube Culture & Trends confirms this. “What’s up” and “Good morning” currently take the second and third spots.

They also looked at how it ranks against other greetings and varies with different genres. Sports videos most commonly start with “What is going” (I assume followed by “on”) and tech starts with “Ladies and gentlemen.”

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What YouTube recommendations look like for others

Watch enough YouTube, and you end up in a bubble of videos catered to everything you like and believe in. TheirTube, by Tomo Kihara and Polina Alexeenko and funded by the Mozilla Foundation, imagines the point of view of six personas:

Each of these TheirTube personas is informed by interviews with real YouTube users who experienced similar recommendation bubbles. Six YouTube accounts were created in order to simulate the interviewees’ experiences. These accounts subscribe to the channels that the interviewees followed, and watches videos from these channels to reproduce a similar viewing history and a recommendation bubble. Everyday, TheirTube retrieves the recommendations that shows up on their Youtube home page.

In case you’re wondering what my YouTube homepage looks like — and I know you are — just watch every J. Kenji López-Alt upload, and you’ll be just about there.

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Analysis of fake YouTube views

Wherever more attention or the appearance of it equates to more money, there are those who try to game the system. Michael H. Keller for The New York Times examines the business of fake YouTube views:

YouTube’s engineers, statisticians and data scientists are constantly improving in their ability to fight what Ms. O’Connor calls a “very hard problem,” but the attacks have “continually gotten stronger and more sophisticated,” she said.

After the Times reporter presented YouTube with the videos for which he had bought views, the company said sellers had exploited two vulnerabilities that had already been fixed. Later that day, the reporter bought more views from six of the same vendors. The view count rose again, though more slowly. A week later, all but two of the vendors had delivered the full amount.

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Music preference geography

Travel to different parts of the country, and you hear different types of music on replay. Josh Katz for The Upshot mapped the regionality based on the popularity of artists on YouTube.

Of the artists on the Billboard Top 100 this spring, we looked at the 50 that were most watched on YouTube in the United States between January 2016 and April 2017. Each map shows relative popularity in different parts of the country. If one part of a map is lighter, it doesn’t mean people there weren’t watching the artist’s videos; it just means fans were more likely to listen to a variety of other artists.

Nice touch at the beginning: Enter a city or ZIP to listen to the corresponding area’s playlist of popular music as you browse.

See also Katz’s maps from last year for popular television.

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Network Effect overwhelms with data

Network Effect

Network Effect by Jonathan Harris and Greg Hochmuth is a gathering of the emotions, non-emotion, and everyday-ness of life online. It hits you all at once and overwhelms your senses.

We gathered a vast amount of data, which is presented in a classically designed data visualization environment — all real, all impeccably annotated, all scientifically accurate, all “interesting,” and yet all basically absurd. In this way, the project calls into question the current cult of Big Data, which has become a kind of religion for atheists.

Harris and Hocmuth gathered tweets that mentioned 100 behaviors, such as hug, cry, blow, and meditate, and paired them with YouTube videos that correspond. They then employed workers on Amazon's Mechanical Turk to read the tweets aloud and gather data on when behaviors occurred. Tweets are continually collected to collect data on why people perform such behaviors, and Google Ngram provides historical usage context.

It is a lot of things going on at once.

I could go on, but it's better if you experience it for yourself. You're given about seven minutes per day to view, depending on the life expectancy of where you live. The weird thing is that even though it's an overwhelming view into online life, you're left wanting more, which is exactly what the creators were going for.

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