Using Twitter lists to balance my attention: a slow motion self-experiment

Area chart from top: grey section diminishing is size; thin purple line; blue sction diminishing in size; pink section increasing in size

If I’m going to live in a filter bubble, it might as well be one that I have intentionally constructed for myself.

For a few years now I’ve been using tools like proporti.onl to keep a check on the gender balance of the people I follow.

nonbinarymenwomenno gender, unknown
People you follow1%50%49% 
Guessed from name0537464443
Declared pronouns14130188 
Proporti.onl results for @mattedgar – Sampled 1776 people you follow, 3000 followers and 110 users from the latest 200 tweets in your timeline. Gender estimate based on 729 Twitter bios with declared pronouns like “she/her” and 2546 genders guessed from first names.

But does this actually make a difference to where I direct my attention? I figured that if people I follow is an input measure, then people I retweet might represent the output, an indicator of what catches my eye, and whose voices I amplify.

Does following a 50:50 split of men:not-men translate into 50% of retweets to women and non-binary people too? As it turns out… not quite.

2018

A couple of Christmases ago I downloaded my Twitter archive, inexpertly hacked the JSON file of my tweets into a spreadsheet, and analysed who I was retweeting.

nonbinarymenwomenno gender, unknown
Share of 2018 retweets2%45%32%21%
Number of 2018 retweets15329235154

Over the course of a year, despite my efforts to follow a balanced group of people, tweets by men still took up a disproportionate share of the things I chose to retweet. (Virtually all of the “no gender, unknown” category are accounts of organisations and other non-humans.)

I could speculate about the reasons for this – More talkative men? A different mix of topics? My own implicit bias about what’s retweet-worthy? – but how to fix it?

I used my spreadsheet to find a sub-Dunbar number group of people whose tweets I knew I always wanted to see. To help correct for the imbalance in my previous year’s retweets, I set different thresholds for men and women and made a Twitter list:

Most retweeted in 2018: 44 women/NB people I retweeted 2 times or more in 2018; 27 men I retweeted 3 times or more in 2018

This became my go-to list when checking Twitter. Using a list this way had the added benefit that I was no longer prey to the algorithmic whims of Twitter’s “home” view.

2019

A year later, I repeated the analysis.

nonbinarymenwomenno gender, unknown
Share of 2019 retweets1%46%37%16%
Number of 2019 retweets8414330147

My number of retweets of women had increased a little, from 32% to 37%, but retweets of men had also gone up. For the next year, I doubled down on the approach, raising the threshold for men to get onto my regular reading list:

Most retweeted in 2019: 56 women/NB people I retweeted 2 times or more in 2019; 20 men I retweeted 4 times or more in 2019

2020

I’ve just completed analysis for year 2 of my experiment:

nonbinarymenwomenno gender, unknown
Share of 2020 retweets1%45%40%14%
Number of 2020 retweets5414366128

Compared to 2019, retweets of women is now up to 40%, compared to 45% retweets of men. While things are moving in the right direction, I’m not there yet.

For 2021, I’m sticking with the same formula for list creation, with a small tweak that brings the black, Asian and minority ethnic (BAME) representation in the list up to 14%, about the same as in the UK population:

Most retweeted in 2020: 67 women/NB/BAME people I retweeted twice or more in 2020; 18 men I retweeted 4 X or more in 2020

Where next?

There are limits to this approach.

  • Like proporti.onl, I’m making assumptions about some people’s genders, so it’s possible that in some cases I’ve got it wrong. For that reason I’m not sharing names by gender, only the overall numbers.
  • My analysis so far focuses only on gender. I ought also to consider other kinds of diversity among the people I follow.
  • I’m missing the toots I boost on Mastodon, the open-source, self-hosted social networking service. If I put more time into this, I would work out a way to include those too.

In the meantime, this is my filter bubble. I plan to re-run the numbers in December 2021.

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