The academic community erupted in response to Elon Musk taking over Twitter (now X), with many anticipating an end to Academic Twitter. Two years later, we're still here. So...?
Great article! Thank you so much. I have been on academic twitter / AcademiX for a while and heard a lot about the rumours, but was too late to experience the old version of it myself.
In my experience since Musk took over things are more regulated. e.g. i cant mention substack accounts or the post will get effectively blocked. In that sense the loss of free speech is already lost to an extent.
Keep up the good work with this newsletter, Marina!
When you cite "misinformation," are you referring to scientific voices discussing topics that vary from a particular narrative or your narrative? Or, are you referring to the crackpots?
How can you accurately distinguish a scientific voice with a different interpretation of the data from a crackpot?
I suppose the methodology and reproducibility are two elements that a reader should always be careful of.
Did they P-hack? Is their conclusion a bit too far fetched? Is there a determinism stemming from a wild abstraction? If we were to reproduce it, how likely is it going to be the same (I am from STEM)?
You can also look at the funders, the authors themselves and their track record, the accuracy of the title to the content (is it overselling?) among other things.
But all of these are time-consuming and difficult thing to validate. The most secure way to protect one from misinformation is to carefully choose one’s sources. Especially if they don’t have a positive feedback loop to boost a metric or push a narrative out of thin air.
When learning an area, I pick the papers apart and separate the useful from the problematic from the outright dishonest lying. That is a very painful process in time and effort.
After figuring out who is doing honest work (not some funding gambit), I subsample the papers and information.
All along, it is necessary to try to think of the data with another interpretation. Most times, it is possible to interpret data in multiple ways because human understanding is limited.
Just curious, have you also accounted for the rise of AI chat and its implications on misinformation and other phenomena... the concurrence with departure seems interesting and perhaps correlated but may also be causative... good social science (which is rare) demands these kinds of questions.
Excellent take on the changing ecosystem, Marina. I do sometimes wonder what is happening on these platforms and the different impact posts have. While I really enjoy posting on X, I do find the community on LinkedIn much more supportive and welcoming, especially the academic side.
What we are seeing with the new algorithms and platforms is rampant repurposing of content. When something does well, it instantly gets copied to a different platform, sometimes even without creator attribution. It's a brave new world optimized for memes and lo-fi content. This can be discouraging for people wanting to create high-quality educational content.
Great article! Thank you so much. I have been on academic twitter / AcademiX for a while and heard a lot about the rumours, but was too late to experience the old version of it myself.
In my experience since Musk took over things are more regulated. e.g. i cant mention substack accounts or the post will get effectively blocked. In that sense the loss of free speech is already lost to an extent.
Keep up the good work with this newsletter, Marina!
When you cite "misinformation," are you referring to scientific voices discussing topics that vary from a particular narrative or your narrative? Or, are you referring to the crackpots?
How can you accurately distinguish a scientific voice with a different interpretation of the data from a crackpot?
I suppose the methodology and reproducibility are two elements that a reader should always be careful of.
Did they P-hack? Is their conclusion a bit too far fetched? Is there a determinism stemming from a wild abstraction? If we were to reproduce it, how likely is it going to be the same (I am from STEM)?
You can also look at the funders, the authors themselves and their track record, the accuracy of the title to the content (is it overselling?) among other things.
But all of these are time-consuming and difficult thing to validate. The most secure way to protect one from misinformation is to carefully choose one’s sources. Especially if they don’t have a positive feedback loop to boost a metric or push a narrative out of thin air.
I agree with you.
When learning an area, I pick the papers apart and separate the useful from the problematic from the outright dishonest lying. That is a very painful process in time and effort.
After figuring out who is doing honest work (not some funding gambit), I subsample the papers and information.
All along, it is necessary to try to think of the data with another interpretation. Most times, it is possible to interpret data in multiple ways because human understanding is limited.
Just curious, have you also accounted for the rise of AI chat and its implications on misinformation and other phenomena... the concurrence with departure seems interesting and perhaps correlated but may also be causative... good social science (which is rare) demands these kinds of questions.
Great article, very insighful. The shifting dynamic of academic twitter has been a concern for my team too.
Excellent take on the changing ecosystem, Marina. I do sometimes wonder what is happening on these platforms and the different impact posts have. While I really enjoy posting on X, I do find the community on LinkedIn much more supportive and welcoming, especially the academic side.
What we are seeing with the new algorithms and platforms is rampant repurposing of content. When something does well, it instantly gets copied to a different platform, sometimes even without creator attribution. It's a brave new world optimized for memes and lo-fi content. This can be discouraging for people wanting to create high-quality educational content.
Thanks so much for your reflections.