I’m not going to link to it, but I am fascinated by a recent post on the Gab blog where Andrew Torba announced some new features to help Gab users push back against research on the platform. Not only do I have two or three ongoing projects using Gab data (one is in the very, very early stages and—ironically—uses Gab blog posts), but some of what Torba wrote also aligned with some of the (fortunately mild) trolling my co-author, Amy Chapman, and I have experienced because of my work on the far-right-influenced DezNat hashtag in Mormon Twitter. So, I thought I’d write up some of what stood out to me in the post.

I’m happy to report that a paper of mine (in collaboration with David E. Williams at the University of Saskatchewan) has just been published in The Internet and Higher Education. We topic modeled 77,514 tweets from 59 academically-themed but anonymous or pseudonymous Twitter accounts. This resulted in five broad topics, and we followed up with a qualitative analysis of the 100 most-representative tweets from each of those topics to generate some narrower codes. The combination of these computationally-derived topics and the human-generated codes gave us a peek into what pseudonymous academic accounts tweet about. The paper is freely available at this link through August 2nd, 2022 if you’re interested in grabbing a copy.

I am increasingly of the opinion that the distinction between “qualitative” and “quantitative” isn’t all that useful and that what we actually mean is usually better expressed in other terms.