I divided my time this month between West Africa and the Bay Area, which triggered a lot of cultural whiplash, which got me thinking about filter bubbles. I fear today’s technology can reinforce our instinct to confuse what’s familiar with what’s normal … which leads to skewed perceptions, bad decisions, and needless conflict. It’s OK to live in a bubble, but it is not OK to not know that you live in a bubble.
At the same time, though, I’m an engineer, which means the entire preceding paragraph already feels far too abstract and handwavey. What’s a bubble? How can one distinguish between what’s familiar and what’s normal? How can you possibly measure and quantify any of this?
…Conveniently, I have an answer to that question, inspired by my friend Leigh Honeywell, and I hereby name the Honeywell Bubble Count after her. It’s a very simple algorithm indeed:
- Go to the social network on which you’re most active. (For me: Twitter.)
- Of the people you actively follow, what percentage are a different gender than your own? (For me: 98/251, 39%. Not too bad.)
- Of the people you actively follow, what percentage are — to use the wonderful Canadian phrase — members of a visible minority, or, a different visible minority than your own? (For me: 35/251, 14%. Hmmm.)
- Of the people you actively follow, what percentage are residents of a nation other than the one in which you live? (For me: 80/251, 32%. Significantly fewer than I expected, but not awful, I suppose.)