Absolutely Maybe, PLOS Blogs, Hilda Bastian, January 12, 2023: “The first myth to dispense with: That there’s almost no research on quote tweets! I added to this misconception with my December post. I’d heard there was next to no research so often, that when I had trouble finding studies, I assumed digging harder would be futile. Big mistake! Soon after I posted, I saw mention of a few studies I hadn’t seen. The problem wasn’t a lack of research, but poor retrievability of studies – and a lack of adequate literature reviews. Events also rapidly overtook that last post. A thoughtful and wide-ranging debate about whether to add quote boosts to the core Mastodon software blossomed. (You can dip into it via @futurebird‘s comments here, and this thread.) And then on January 3, Mastodon’s lead developer announced he was now open to the idea of opt-out quote-boosting, after having adamantly ruled it out in the past. That would mean you would be able to set preferences on your Mastodon account about whether to offer the quote function, restricting it or disallowing it completely. I had argued for controlled experimentation in my post – aka A/B testing. That’s not likely to happen. But as we continue down the road of uncontrolled experimentation, at least we could have a better grip on the evidence about the Twitter experience. So I went back to the drawing board, and got started on a serious dive into the literature. I’m sure there must be more studies I didn’t find – especially non-English ones. But I found enough to give reasonable, or at least partial, answers to most of my questions. I found 33 studies with data and/or content analysis for quote tweets (QTs) that I use in this post, and another 4 that I don’t. Of those 4, I didn’t use 3 of them because the quality was too low – and the fourth, because there wasn’t enough information for me to be able to interpret the data. They’re all detailed below this post, along with some explanation for how I found them. Most of the studies use Twitter’s API, a data service that pours out a random selection of public tweets that is meant to be a fairly reliable representation of Twitter activity. (The one time I was involved in using that data, there were over 4m tweets across 3 days – out of roughly 1.5 billion.) Few studies with large samples of tweets report on whether they considered the possible contribution of bots…”