Tweets to Citations: The Impact of Social Media Influencers on AI Research

Despite the paper's claims to the contrary, they most likely have the causality absolutely backward. The two accounts they analyse, AK (@_akhaliq) and Aran Komatsuzaki (@arankomatsuzaki), have much better taste than anyone or anything else I've come across that are as prolific as they are. There's a reason lots of us ML/AI researchers follow them closely, which is that over an extended period of time we've noticed that they keep surfacing interesting and important work.

They do a much better job of it than some random reviewer, so you would not expect some other papers with the same review scores, venues, and embeddings to have the same citation counts. But this is the premise that this paper entirely relies on as the basis of the analysis, and as a result the paper fails to make a strong case.

The idea that thousands of researchers would decide to follow two particular Twitter accounts for no good reason, and would like and retweet posts from those accounts just because they're "influencers", makes no sense at all. Accounts become influencers for a reason. In this case, the accounts are not from people who are otherwise famous for some external reason -- literally the only thing they're known for is posting papers. If they weren't good at finding papers that researchers found interesting, they wouldn't get followed.

BTW, some years ago, Miles Brundage's Twitter account was the main one that researchers followed for this purpose. Nowadays he's the head of policy at OpenAI and no longer does this. But back when he did, the '@brundagebot' bot was created that tried to replicate his curation choices using an algorithm.

As much as I dislike all the baggage that comes with Elon owning Twitter, it is the only place I know with a critical mass of AI talent sharing papers and thoughts. Even in read-only mode, it's incredibly useful as a way to stay up to date with interesting AI papers and discussions.

Does a good alternative to this exist?

Don't underestimate the power of informal networks. Buzz is hard to quantify but it clearly has a huge effect on "real" metrics like citations.

"Computer Science > Digital Libraries"

how does this even merit inclusion on arxiv? Does it even count as science? We're talking meta-computer-science. Meanwhile, It's hard enough for me to get the endorsement to publish my actual math paper on there, which has afik new results. The computer science section of arxiv feels like a giant citation ring of mediocre, math-light papers.

I posted a related paper a couple of days ago [1] titled "Promotion of Scientific Publications on ArXiv and X Is on the Rise and Impacts Citations"

[1]: https://news.ycombinator.com/item?id=39124309

So glad the AK account exists. As a researcher, I've always wanted some guy with an econ degree and a year of ML eng to recommend me papers after glancing at them for maybe 30 seconds.

I am genuinely baffled that researchers in the field think there is any value in the service AK provides. I'd wager I could create an equally effective bot with the following process:

    1) Create a historical dataset of publications and their citation counts

    2) For each publication extract the following features:

        - H-index of first author

        - Maximum H-index of all authors

        - Number of author affiliations in {top-10 school, deepmind, meta, openai, nvidia}

        - Number of times the phrase "state-of-the-art" appears

        - Which latex template is used (NeurIPS, ICML, etc.)

        - Number of images in the paper

        - Whether there is an image on the first page

        - Whether "all you need" appears in the title

        - Whether the publication has a linked project page

    3) Train a shallow decision tree with citation counts as the regression target