As always seems to happen, people looked at the list which already had the problem of being grossly skewed towards men, and noticed other odd little things. Like, “hey, I follow this science person who has more followers than so-and-so, why isn’t she on the list?” Clearly, there were errors of omission, and oddly, they all seem to skew away from women and people of color. Or maybe not so oddly — it’s striking how often that seems to happen.
And then people started thinking about the premise. I recall from my phone conversation with the author that I’d told her that follower count was a poor measure of influence — I mentioned a few names to her that I thought used Twitter really well, but that I had no idea what their follower count was. It turns out that @bug_gwen and @docfreeride, for instance, don’t have enough followers to have made the Top 50 list.
Wait a minute — so why are we using follower count as a metric? I don’t pick who to follow by looking at the number of followers they have! If I did, I’d be following @JustinBieber, who has 54.7 million followers. There is a kind of bandwagon effect, in that you’re more likely to encounter someone on Twitter if they have lots of followers, because it is more likely that you’ll be following someone who follows the popular gang…but that’s also a poor reason to consider someone a good person to follow. There are a great many people who have huge follower counts because they have a fanbase in another medium — books, for instance, or television shows — and are absolutely abysmally bad at Twitter, and I don’t need to name names, since you’ve all probably got a few in mind already. (there are people who are good at multiple media: @NeilTyson deserves to have a lot of followers for his good use of Twitter, and I’ve found @NathanFillion to be hilarious and enthusiastic).
But, unfortunately, what it means is that Science picked an easy metric to measure, rather than a difficult one that might tell you something significant, and it was also a metric that amplified biases in the pool. We already know that people are less likely to pay attention to women’s voices, so when you use a parameter that is dependent on how many voices listen to you, you’re already screwing that part of the population. And then I suspect the filter goes to work: you start searching for popular twitter accounts by looking at who the most popular twitter accounts follow.
I do not blame the author. She was trying to track down a quantifiable measure and used the ones at hand, and was also trying to address a specific contrivance, the claim that high Twitter follower counts was somehow indicative of scientific failure. She didn’t invent the Kardashian index, so don’t blame her: blame Neil Hall, who came up with the K-index in the first place.
And now, of course, we get a useful backlash. People have started compiling lists of active Twitter users who also happen to be scientists and women. Here’s one from Paige Brown Jarreau; one by Erica Check; another by Victoria Herridge. I also posted a list of women scientists on youtube a while back. It seems these are relatively easy to find. Instead of referring to arbitrary lists of people assembled by an arbitrary metric that has a built-in bias against certain kinds of people, you’ll find that there are other lists built by advocates to counter those biases.
I am reminded of the time that atheists conference organizers were complaining that they couldn’t find any women speakers, so they kept mining the same old small group of old boys for their rosters, so Jen McCreight compiled a list of awesome female atheists to make it easier for them…and to kill that excuse. And then they complained that they could only find white atheists, so Greta Christina put together a list of atheists of color for them.
Isn’t it weird how invisible people suddenly become apparent if you just look for them?