Mastodawn

✍️ A requiem for #Twitter: what #science has lost

With limits on tweet reads (!), Twitter users are now feeling the pinch that #research has been feeling since Musk raised API prices.

I've written a blog post on what losing access to the API means for #AI, #journalism and emergency responses. #TwitterDown #RIPTwitter

Give it a read and boost: https://nicholasmamo.github.io/posts/a-requiem-for-twitter/

A requiem for Twitter: what science has lost

Twitter is dead. Technically, it is still alive—barely—but for scientific research, it is as good as dead. Twitter’s demise did not start with the saturation of blue ticks, nor with the nonsensical limits on daily views. It started and ended with cordoning off access to the Twitter API; now, it costs $5,000 per month for 1,000,000 tweets—a tenth of what academics previously got for free. I started researching on Twitter in 2016.

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MJ Valente Jul 3, 2023

@memonick Really liked your post… it has a bittersweet taste, but we know that adaptation to new environments decides survival. Science (and journalism) will for sure adapt, Twitter (or Musk… who knows the difference nowadays) is taking a different approach. Maybe Twitter was doomed to perish without financial profit, but I’m doubting it will survive with Musk’s approach. The old Twitter will be missed, the new one not at all.

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Nicholas Mamo Jul 3, 2023

@mjvalente Very nicely put! Judging by what we're seeing with Twitter and Reddit, I do believe that social networks are getting to a point where they must become profitable or fail.

There are other options where social media is concerned, but for most experiments, we need a lot of data. Just to give you an idea, building live-blogs automatically (which is what I do), often requires a few hundred thousand tweets every couple of hours. Maybe that will push more innovation, at least?

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MJ Valente Jul 3, 2023

@memonick I think need pushes innovation. I’m still amazed with medicine’s advances during pandemic (I should not, since it has happened before and I am an historian…).

If innovation doesn’t happen because we can’t access the optimum amount of data, it might happen by changing the way we work, using less data. Genetic work it’s a proof of that, for instance: nowadays, more can be done with less. Modeling and data extrapolation are essencial.