Your browsing history is a representative of your gender and one can identify the gender just by looking at the sites you visit.

This site has built a great JS that reads your browsing history and estimates your gender.

One of the things that I always wanted to do but never got around to was to analyze a user’s browsing history to estimate age and gender. Of course the idea is definitely not new, in fact Xerox (of all companies??)has a patent on the whole process and I’m certain plenty of networks already do something of the sort… but what the heck, let’s have some fun!

So what I did is I modified the SocialHistory JS so that it polled the browser to find out which of the Quantcast top 10k sites were visited. I then apply the ratio of male to female users for each site and with some basic math determine a guestimate of your gender. The math is really quite simple, I just take:

1 / (1 + r_1 * r_2 * … * r_n)

where p_i is the ratio of men-to-women for the specific site. For example, if you had been to two sites that had a 2-1 ratio of men to women, the probability of you being female would be:

1 / (1 + 2 * 2) = 1/5 = 20%

Link to the site.