Threaded

I penned a Thot(?!), or rather, a post on Threads, the Twitter clone that Meta released some time ago. I don’t find it particularly useful, as my Twitter usage had declined long ago.

Anyway, the post (and accompanying photo):

“When I contemplate the idea of relocating, it’s 70° nights dining outside on a slanted sidewalk at the beginning of November with sunsets like these that make me realize I’ll never leave California.”

At this time of writing, there have been 145 replies and 1,868 likes.

I’ve rarely had my social media output reach heights like this, and the impact that Threads can have at this early stage is intriguing. I’ve been learning here and there about how the algorithm supposedly works, and currently, it’s showing me a reasonable amount of interesting content. So, when I posted that on a Friday evening, imagine my surprise when I saw the post skyrocket the next day.

The replies are earnest, almost confessional in tone, more for the authors themselves than for me. It's as if I just threw out a prompt that reminded people of their own reasons and gratefulness to live in California. I guess that’s how Threads works currently: the actual town square that Twitter used to be. A thread has enough room for anyone on it to board the train too.

For a day there (and a few replies are still trickling in), it felt like a warm embrace. For a moment, these people and I could share commonality rather than division.

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At the same time, the juxtaposition of the platform has started to reveal the stranger parts of a feed tuned for engagement and strangeness. For all the possible relevant posts that Threads will show me, there’s always a strange taste of bitterness. Usually in the form of a snarky or mean-spirited thread or post that reminds me of why I started to veer off Twitter so many years ago.

By sheer user and population size, Threads has a huge advantage over the Fediverse, given its smart porting of Instagram accounts being reused, and for what it’s worth, it’s likely a more diverse group of people by nature of that breadth.

The danger of algorithms and being unable to find the edge cases (or possibly not even caring about them) is that you trade control of the things you see and that matter to you in favor of some dopamine and serendipity.

The latter, I suppose, is much like the real world.