Editor's Notes: What Is Social Media For?

On the Out of the Jaws podcast, I like to joke with my younger co-host Ryan Geddie that I am "old new media." He and his peers are in the world of video as it exists today, and quite good at it. Often when they say "new media" they don't even mean just video in general, but streaming and streamers specifically.

Half of my life ago, "new media" mainly meant blogs and wikis.

And when I think about that time, the analysis that has really withstood the test of time (though its host site, like so many, has not) is Clay Shirky's "Power Laws, Weblogs, and Inequality," first published in 2003. Here is the part that sticks with me:

To see how freedom of choice could create such unequal distributions, consider a hypothetical population of a thousand people, each picking their 10 favorite blogs. One way to model such a system is simply to assume that each person has an equal chance of liking each blog. This distribution would be basically flat—most blogs will have the same number of people listing it as a favorite. A few blogs will be more popular than average and a few less, of course, but that will be statistical noise. The bulk of the blogs will be of average popularity, and the highs and lows will not be too far different from this average. In this model, neither the quality of the writing nor other people's choices have any effect; there are no shared tastes, no preferred genres, no effects from marketing or recommendations from friends.

But people's choices do affect one another. If we assume that any blog chosen by one user is more likely, by even a fractional amount, to be chosen by another user, the system changes dramatically. Alice, the first user, chooses her blogs unaffected by anyone else, but Bob has a slightly higher chance of liking Alice's blogs than the others. When Bob is done, any blog that both he and Alice like has a higher chance of being picked by Carmen, and so on, with a small number of blogs becoming increasingly likely to be chosen in the future because they were chosen in the past.

People like to blame algorithms for attention concentration within certain platforms, or the "walled garden" nature of proprietary platforms for their dominance in the ecosystem. But if one person joining a social network increases the odds of another person joining "by even a fractional amount," we will see adoption cascades that concentrate users into a small number of platforms. Whether those platforms are open source, proprietary, walled gardens or open protocols does not really factor into this, except in as much as those particular implementations make adoption more or less likely (looking at you, Mastodon).

This logic governs platform adoptions and attention within those platforms, algorithms or no algorithms.

Very well. But cascades occur at different scales. Some things make it to the "head" of the power law distribution; the superstars who obtain 80 percent (or in our highly interconnected age, much more than that) of the attention. Some things land mid-tail somewhere. Most people find this dynamic quite legible from their experience of viral content. A public Facebook post by a user with a few dozen friends could get shared a handful of times, a few hundred times, 10,000 times, or 10 million times. Each step up in scale is drastically less likely than the last, but it is quite possible.

Hardly any posts get shared 10 million times. But quite a lot get shared 10,000 times. In platform terms, Bluesky simply is closer to the post shared 10,000 times than the post shared 10 million times. It is not in the top 1 percent of platforms, it is probably not even in the top 10 percent of platform. That puts it in a much smaller class of platform than the main ones. Mastodon's situation is even worse than this.

Does that make these platforms entirely worthless? I think not.