Access to read 1 million posts through the X API costs $5000/month. Enterprise access to their API costs $42 000 per month.
Multiple researchers are being told by X that they must pay this fee to get access[1][2][3].
X has recently been fined for not providing this access to researchers. Both for the organic engagement, and for paid advertising. [4]
The pricing of X's API is exorbitant and orders of magnitude higher than arguably higher quality datasets like Reddit. One million posts through the Reddit API costs $2.40.
The pricing scheme is obviously not value based and is clearly designed to limit data access to researchers. As users here note, studying recommender systems requires studying the inputs and outputs of the system. Platforms are rightly not mandated to present the inputs due to privacy concerns. But they are mandated to make the outputs available. And they aren't. "Open sourcing" their algorithm is not a replacement for this, it's an obvious a ploy to present themselves as transparent.
That is one of the worst clanker-brained replies I have ever read on this platform.
The OP you're replying to made a concrete point (X claim to be transparent but block researcher access through unreasonably price gating), and you didn't even attempt to refute it nor engage with the substance of the post at all.
If you think that this evidence is so compelling why don't you link to some sources and summarize it in your own words? If you cannot be bothered to do that much, why are you replying in the first place?
Telling someone that they're wrong and they should just chat with an LLM to educate themselves removes any room for discussion, leaving this platform and other comment readers worse off.
Nope. I didn't feel like spending the time to read the code, but I did want an LLM to pull out specific pieces for me and compare them to other published info. This is a good way to use LLMs: ask them to organize data for you to consider yourself and come to your own conclusion.
In this case, the info I looked at changed my opinion (downwards) on how cynical this release really was.
Access to read 1 million posts through the X API costs $5000/month. Enterprise access to their API costs $42 000 per month.
Multiple researchers are being told by X that they must pay this fee to get access[1][2][3].
X has recently been fined for not providing this access to researchers. Both for the organic engagement, and for paid advertising. [4]
The pricing of X's API is exorbitant and orders of magnitude higher than arguably higher quality datasets like Reddit. One million posts through the Reddit API costs $2.40.
The pricing scheme is obviously not value based and is clearly designed to limit data access to researchers. As users here note, studying recommender systems requires studying the inputs and outputs of the system. Platforms are rightly not mandated to present the inputs due to privacy concerns. But they are mandated to make the outputs available. And they aren't. "Open sourcing" their algorithm is not a replacement for this, it's an obvious a ploy to present themselves as transparent.
[1] https://arxiv.org/abs/2404.07340
[2] https://devcommunity.x.com/t/academic-twitter-access-is-dead...
[3] https://devcommunity.x.com/t/apply-academic-research-access/...
[4] https://ec.europa.eu/commission/presscorner/detail/en/ip_25_...
That is one of the worst clanker-brained replies I have ever read on this platform.
The OP you're replying to made a concrete point (X claim to be transparent but block researcher access through unreasonably price gating), and you didn't even attempt to refute it nor engage with the substance of the post at all.
If you think that this evidence is so compelling why don't you link to some sources and summarize it in your own words? If you cannot be bothered to do that much, why are you replying in the first place?
Telling someone that they're wrong and they should just chat with an LLM to educate themselves removes any room for discussion, leaving this platform and other comment readers worse off.
Are you being sarcastic?
Nope. I didn't feel like spending the time to read the code, but I did want an LLM to pull out specific pieces for me and compare them to other published info. This is a good way to use LLMs: ask them to organize data for you to consider yourself and come to your own conclusion.
In this case, the info I looked at changed my opinion (downwards) on how cynical this release really was.