I rarely read twitter due to the API changes which I’ve talked about in the past. But I saw Teknoteacher talking about changing his followers after reading about Male tech CEOs follower accounts. I thought I’d share some things I discovered too. Especially reading this a while back.
So my results are above, using the online tool – https://www.proporti.onl.
But a while ago I used Open Human’s twitter archive analyzer by Bastian Greshake Tzovaras. It was super sobering!
Here is my replies by gender from when I first started using Twitter back in 2017. As you can see there was a massive spike of conversation with males in 2012, I also generally talk to more men than women on twitter.
Likewise when retweeting based on gender its mainly males. Recently its a lot closer to 50% which is great but I wonder with my lack of twitter use, how that will effect things? (I have requested a new update of my twitter data)
Of course my instant thought is there is noise in the figures as its not always clear if people are male or female for many reasons. But its disappointing to read Elon Musk’s tweet.
I use twitter for news orgs. My Insta has same women as men. What's up with the phoney PC police axe-grinding?
— Elon Musk (@elonmusk) October 4, 2016
And read about others such as…
Sundar Pichai, the CEO of Google, follows 267 accounts on Twitter. Of those, 238 appear to be men. He follows nearly as many Twitter Eggs (15) as women (21).
Satya Nadella, Microsoft CEO, followed the most women (39) of any of the accounts examined by the Guardian, though that is still half the number of men he follows (78) out of a total of 165 accounts.
I’d really like to see this applied to race not just gender too. It reminds me how I was going to learn more Python so I can create this as a Juno personal notebook in Open Humans.
I updated Open Humans with my latest Twitter data export and here are the results.
Once again very sobering to see. Got to make some changes.
Worth adding from TwArχiv site.
The graph shows you the number of replies to Twitter users that are classified as either
female. The classifications are predictions based on users’ first names as given in their Twitter accounts. The predictions itself are performed by the Python package
gender_guesser. It uses name/gender-frequencies from a larger text corpus.
unknownclassifications are ignored. To decrease the noise the daily values have been averaged by a daily average over a 180 day window (
Even more interesting than whether replying to people might be gendered can be the question which voices are being amplified . On Twitter a good indicator of amplification are retweets. These can be gender balanced or show biases, similarly to the replies to other users.
The graph shows you the number of retweets to Twitter users that are classified as either
female. The classifications are again predictions made by the Python package
gender_guesser. To decrease the noise the daily values have again been averaged by a daily average over a 180 day window (