Lots of good observations in here, and I need to think through the implications for digital methods research.

I confess that I would have found this interesting in an earlier part of my career. Now, though, I’m reminded that I built that career on a methodological approach that’s uncomfortable close to surveillance, and I don’t love that.

Putting together a reading list for a class on social media research reminds me how much writing on this serves as an unintentional history. I don’t know if any of this chapter on FB and Twitter APIs is still relevant, but that alone may be worth reading it for…

This is just a few words to say that this post that I wrote back in December 2022 has suddenly become relevant.

In short, some of my recent work has been on an online Mormon community that has some overlaps with the far-right. In between my collection of the data and eventual publication of our various articles, my co-author and I have noted some prominent accounts’ being suspended from Twitter. Because we work hard to not use identifiable quotes in our writing, and because of Elon Musk’s decision to unsuspend nearly all suspended accounts after taking the platform over, I’ve been checking accounts I knew to previously be suspended as we work on a new manuscript. Today, for the first time, I’ve found one account that has indeed been unsuspended.

Every semester, I give a guest lecture on internet research methods for an undergrad class in my unit. A few days after scheduling this semester’s lecture, I’ve realized it’s the first time I’m giving it after Musk borked Twitter as a data source.

This is a real shame.

This is… I don’t know what this is. Besides a whole bunch of nonsense.

This article has been available online for nearly two years, but since I don’t have any previous posts about it, I’m happy to announce that a study of mine with Dan Krutka has just been assigned to an issue at the Journal of Research on Technology in Education. A number of years ago, Twitter released some large datasets of tweets associated with accounts created as part of various governments’ information operation efforts. Neither misinformation nor information operations are a specialty of mine, but I was interested in seeing if these datasets overlapped with work that I was involved in. So, one day, I downloaded the datasets, loaded them all into R, and searched for some terms germane to my research. That’s how, a couple of years ago, I produced a paper on government-sponsored accounts’ invocations of Mormonism as part of efforts to sow discord in the United States.

RIP my Twitter research. Glad I have other irons in the fire…

Some good coverage of the consequences of API restrictions for researchers—though I think we still need clarification from Twitter about whether the academic dev status is being handled separately from primary dev status.

I presume this decisuon also cuts off academics; this is going to have a huge impact on research, and not in a good way. I’m glad I’ve pivoted to other platforms, but this is still infuriating.

For over three years now, I’ve been getting increasingly involved with research projects that involve the online far right in one way or another. One of the most interesting ways that I’ve developed as a researcher during this time is having to think through in greater detail my commitments to research ethics. Because my research typically focuses on public social media data, I am rarely required to obtain informed consent from those whom I study. Of course, I agree with many internet researchers that this does not absolve me of my ethical responsibilities (I find Fiesler and Proferes’s 2018 paper on this subject particularly helpful). This becomes even trickier, though, when the unwitting “participants” in my research espouse views that I find objectionable. To what extent do I, as a researcher, owe a Twitter (or Gab) user privacy and dignity if they are engaged in homophobic, misogynist, or white nationalist behavior? I’m still figuring this out, but my approach right now—informed heavily by this paper—is to try to err on the side of respect for the user whenever possible.

I hadn’t realized so many academics were working with data brokers. It’s kind of scary! The EFF has some good points here about so-called “data for good”—and rightly brings up that ethics review boards should be thinking about this sort of thing.

I got word that a recent publication of mine was now published in an issue of Learning, Media, and Technology. It has actually been available online first for the past ten months, but since I haven’t been good about blogging about recent publications, I figured this was as good a chance as any to write a post about it. This piece is called “Lifting the Veil on TeachersPayTeachers.com: An Investigation of Educational Marketplace Offerings and Downloads” and is a collaboration with Catharyn Shelton, Matt Koehler, and Jeff Carpenter. Matt web scraped over four million pages associated with resources from the TpT platform, and we took a quantiative descriptive look at resource metadata, including subject areas, grade levels, resource types, Common Core State Standards, user ratings, and prices across those resources. We then interpreted our findings through José van Dijck’s writing on embedded values on digital platforms—this was my first real dive into van Dijck, and I’m particularly proud of that contribution. Here’s the abstract for more of a peek:

What is the most soothing form of digital data collection, and why is it forum scraping?

Just because you can topic model something doesn’t mean it actually tells us anything (and please don’t ever describe computational text analysis as “objective”).

One of those afternoons where I’m auditing someone’s analysis code, but it’s an analysis of 4M rows of data, so I’m also doing spurts of grading while I wait for code to execute.

35 GB of data is a lot to begin with, but when it’s 35 GB of CSVs? That’s when it starts to really register.

I got a reminder today that I do the kind of research where something as hilariously unintuitive as telling a program to treat long numbers as “words made up of 0-9” is actually a critical step to making sure you get the right results.