Do women science bloggers tweet differently to men? (Part 1)

I cobbled together a program to take a very unscientific look at the Twitter accounts of a few hundred men and women in science blogging. Here's what I found.


Last week I was a panellist at TalkFest, a London-based event which on this occasion was devoted to the perennial issue of the fate of women in science blogging (I believe the podcast of it will be online soon).

One of the problems I highlighted was lack of data on online science in general. Jenny Rohn noted last September that most prominent science bloggers on the main networks are male. Casual inspection of e.g. the Wikio rankings of top science blogs shows them to be similarly man-heavy. In terms of actual evidence, that's about as much as we have, which is probably why this discussion tends to go around in circles.

So we don't really know why women aren't more prominent, but whatever the reason, it's concerning. It's an ethical concern, and it's also potentially a pragmatic concern - a community dominated by a particular type of people may not be so good at reaching out to others on issues like science funding or climate change.

This weekend I decided to stop moaning, and have a play with one rich dataset we can draw on – Twitter. If I wrote a program to harvest data from the Twitter accounts of hundreds of male and female science bloggers, what might I find?

So I wrote the software, and created lists of male and female science bloggers on Twitter to feed in. These were culled from a) people I follow, b) crowd-sourced lists of bloggers, c) searches for other lists of science writers on Twitter. I ended up with 330 people - 150 men, and 180 women.

Using the Twitter API, I grabbed the last 100 tweets from each person (representing on average just over 10 days' worth), and indexed every word, mention, link, and hashtag in a database, along with various user statistics such as follower and friend counts, tweet frequency, retweets and so on. That left me with a lot of lovely data to play with, which I'll blog here as and when I get around to wading through various bits of it.

This is obviously riddled with flaws and in no way scientific so take the results with a pinch of salt. The lists are likely to be a bit biased by my interests and search strategy. The tweets collected are a brief snapshot of a changing landscape. It's also a fishing expedition, and it's inevitable that if you look at enough variables you'll find *some* difference.

Still, hopefully it's a useful demonstration of what one semi-competent programmer can cobble together in a weekend, and with any luck it might generate a few ideas, or inspire some cleverer people than me to do more rigorous experiments. If you want to take things further, do feel free to e-mail me (

Word Clouds

Creating some word clouds from the data seemed an obvious first thing to do, so I ran a database query to dump the words, mentions, and hashtags used by men and women as plain text files, which I could then just paste into Wordle.

I'm going to present these results with minimal discussion, and let people interpret away in the comments. Again, take them with a pinch of salt.

Words Used

I started out comparing the plain words used by women and men (no hashtags or mentions included).

The differences are pretty subtle. As you can see, the average science blogger tweet, whether from a man or a woman, seems to consist of something like:

Blog Post: New Science!!! Good new science just today! Thanks!! Good time now people!! Via @EdYong209

(As an brief aside, it's interesting how immediate and enthusiastic a lot of the language used by twitterers twats tweeters is. 'Just', 'today','new', 'now', 'see', 'good', 'great', 'like' and so on - it's hard to avoid getting a vibe of 'OMG you have to see this now!')

Hashtags Used

On average, women used hashtags about 33% more than men across my lists, which I'll look at in more detail in the next post. 7,500 hashtag uses were recorded - no individuals particularly dominated the overall count - Sheril Kirshenbaum used nearly 50% more than any other woman, but uses a rich vocabulary of tags.

Note the dominance of conferences and physical meetings - 'eb2011', 'scibarcamb', 'talkfest', 'aaasmtg', 'iaconf','ias2011', 'scio11', 'ukscitweetup', 'cwa2011' - in the women's cloud. There's also a bit of a policy-nerd trend bubbling away in the background with tags like 'scipolicy', 'scienceisvital', 'nhsreform'.

Prominent among the men is a campaign to annoy homeopaths ('homeopathy', 'whaw' and 'ten23'), as well as tweets on drugs and the Fukushima crisis. 'Wonders' presumably refers to the series by Brian Cox. In comparison with the women it's all a little bit yakawow...

It would be nice to dig into some of the hashtags and investigate who uses them, as the results above could easily be influenced by a small number of individuals heavily promoting particular tags - if tags often tend to be dominated by 1 or 2 people, this could obviously seriously bias the outcome of the plots.

Users Mentioned

There were about 27,000 mentions in the tweet corpus. It's hard to avoid Ed Yong, whose name is so ridiculously big on both of these charts that one attempt at rendering left Ed on his own while all the other names sulked in a corner.

I'm reluctant to pick on people here because I suspect these graphs may change dramatically depending on what you've recently blogged/talked about. Nonetheless, the results for Bora and Alice Bell are interesting, ditto Carl Zimmer and Richard Wiseman, while the balance of some of the publications here would be fascinating if it could be shown to continue over a longer period of time (is Nature News really more gender-balanced than New Scientist?).


A WordleFest is not a great thing to try to hang a conclusion from, but there are some interesting questions here. Do women and men tweet the same way about different things? Is there a mismatch between the prominence of women in the physical and virtual spaces of conferences? Will Ed Yong ever let someone else be popular for a change?

Again, this is quick, dirty and not very rigorous, but hopefully it can start some discussion and maybe prompt other people to have a go and do it better. There's still a ton of data sat in my database waiting to be looked at, so watch this space for a follow-up post(s) looking at different chunks of it in the next few days.

Contact: @mjrobbins |