Sean Carmody aka the Stubborn Mule has demonstrated, using chart porn, that my Twitter followers follow Benford’s Law.
Or more precisely, that Benford’s Law is followed by the distribution of the number of Twitter followers that each of my Twitter followers has in turn.
“Benford’s Law of Anomalous Numbers states that for many datasets, the proportion of data points with leading digit n will be approximated by log10(n+1) – log10(n),” says Carmody with a straight face.
So, if you look at the chart, you’ll see that there’s more followers with a follower count starting with a “1” (so 1, 11-19, 100-199, 1000-1999 etc) than with a “2” (2, 20-29, 200-299, 2000-2999 etc) than with a “3” (3, 30-39, 300-399, 3000-3999 etc) and so on.
He does note in another chart that there seems to be a spike of followers with just one follower each. I’m wondering whether that’s about spammers.