Most of my Twitter feed is white. By a lot.
There is also an interesting narrative in who is showing their face and who is obscuring and filtering it with renders and filters. But that is an entirely different study.
At the end of the day, I get where my white dominated feed comes from.
We are all products of our biases. I grew up in White and Latinx spaces, it makes sense these reflect in who I follow. I follow a lot of academia and a lot of folks who do things in the computer science and art/tech spaces. These spaces are predominately white.
Of course, diversity is multiaxis and intersection. I follow a lot more women than men. I follow a lot of gender nonconforming folk. I follow a lot of trans folk. But there is a major axis I am biased towards, and it’s an axis that determines so much of our systemic experiences in the world. And I cannot ignore this axis.
My feed doesn’t make me a bad person. It also wouldn’t make me a good person if it was this perfect pie chart. Performance is not action. And curation is only a form of performance.
But I think it’s important to confront and see what voices we are listening to. Who are we choosing to listen to, whether subconsciously or consciously.
When I was showing this to a friend, she laughed nervously “Omg I would never do this.”
Me: “Why not?”
Her: “Well, if your feed is this white, I don’t even want to know how white my feed is.”
Me: “Why not?”
She didn’t have a good answer.
I think that she should do this exercise or one like it (maybe not as in depth). I think everyone should.
We are never going to make strides in inclusion if we are unwilling to look at where you start. It’s kind of like starting to train for a marathon without knowing what your starting time is. Except this marathon can’t just be run on Twitter.
Because these biases predate Twitter. They predate the first photo, because photos themselves are rooted in them.
I can’t help but find the irony at my use of these average faces in this piece. A lot of people use these for similar veins of communication. But rarely is their history addressed.
Composite photography, particularly of faces, is largely credited to the cousin of Charles Darwin — Francis Galton. Look him up. He and subsequent photographers tried to use composite photography with faces and finger prints to identify a “criminal type”.
This echoes back to the fear that many share when it comes to profiling in facial detection and surveillance. And this fear has been a reality since the beginnings of photography.
I think our social media is a strange curation. The algorithms and recommendations reveal not only how we are being surveyed throughout our interaction but just the reality of our networks.
I like to think of data and computational methods as tools for telling narratives, not the narrative itself. And I think that social media could be a tool to tell us things about ourselves, but only if we are honest about the state of our feed.
This is an exercise and not a scientific study. Data is apt to error due to the amount of manual categorization. This is not meant to serve as a scientific source or any formal recommendations.
This data was collected by downloading publicly available profile pictures of the people I follow.
These profiles were sorted manually by me with the help of some Python scripts to speed up the process.
Gender identities and racial breakdowns are done manually based on profile feeds and personal knowledge. These are apt to error.
Nina M. Lutz is currently a graduate student at the Media Lab.
Where many computer scientists want to make computers think more like people, Lutz aims to use computers to remind us to think of other people. Especially people who may not look like us. Lutz’s methods in doing this reconsider the design and technology choices around the human face in exploration of human identity through technology.
As a first generation college student, she is passionate about education and combating inequities in STEAM and opportunities in academia. Lutz welcomes emails from students and mentees about research and education both in and out of the design and computation space.