As the photos and judgments are stacking up at FaceStat, I thought it would be interesting to see relationships between tags – when tag X appears very frequently with tag Y. I downloaded more than 4,000 rows of tags, where each row corresponds to one face, and ran a Python script to count concurrences.

Some interesting trends (where the first word often occurs with the others):

• Old: dad, wise, jolly, pedophile, grandpa, perv, professor, writer, sour, experienced, dead, matronly, alcoholic
• Smart: business, librarian, azn, graduate, genius, engineer, intent, bookworm
• Oily: wet, sweaty, shiny
• Drunk: drunkard, tipsy, alcoholic, partyboy, scene, stripper, wasted, ditzy, ew
• Young: tween, underage, uninterested, teen, childlike, jailbait, child, tooyoung, highschooler, babyface, kid, boy, innocent, virgin
• Athletic: runner, driven, jock, sporty
• Serious: angry, grumpy, direct, piercing, alert, dedicated, azn, doctor, suave
• Nerd: goodlooking, virgin, slacker, nerdy, goof, geek
• Gay: cowboy, flamboyant, metrosexual, yuppie, feminine, queer, homosexual, out, pissed, dangerous

Other highly concurrent pairs: army & dedicated, fighter & patriotic, skeezy & hairy, mustache & dad, sunglasses & secretive, cougar & milf, naked & creepy, pimp & playa, plastic & fake, tease & sexy, badass & cool.

For more information on how I counted concurrence, in this diagram I would count the tag “tall” occurring 4 times with the word “lanky”, even though the concurrences are all on one line. I converted all words to lowercase.

I sorted the list of tag pairs by a formula: number of concurrences of the pair / number of total occurrences of the first tag. This sort helped to find the interesting pairs.

-Mike

In 2007, researchers at Aberdeen University’s Face Research Laboratory showed that women found the face of a man more attractive when the face of a woman was smiling at it. (NewScientist) Biologists have a term, “mate choice copying”, for similar behavior in birds.

I was looking at some photos of couples on FaceStat and wanted to run a quick and dirty version of this experiment, and including the other FaceStat variables. I photoshopped out the girl from guy/girl photos and reuploaded. The results followed the same trend as the Aberdeen study (although the Turkers are mixed gender). The strongest, most consistent difference for the photoshopped photo was in relationship status (more single, obviously) and in attractiveness (25% of people found them less attractive). Some examples:

Some details and other results:

The guy’s face was clearly identified in the couple photo. I know this because 100% of Turkers classified the person in the couple photo as male.

The single guys were also consistently identified as more conservative and heavier.

-Mike

In the previous post I talked about how we scraped about one hundred photos from social networking sites and had Turkers guess those people’s traits – including age, race, intelligence, political affiliation, and intoxication.

Intoxication was listed as a checkbox. Averaging the guesses for each photo gives us an intoxication rating from 0 (sober) to 1 (smashed). In the histogram, you’ll notice the familiar power law distribution of drunkenness:

Finally, a cautionary graph. We found a clear correlation between perceived intoxication and perceived intelligence:

Intelligence scale: 0 = “doofus”, 1 = “dull”, 2 = “average”, 3 = “bright”, 4 = “genius”

-Mike

How do photos uploaded to social networking sites reflect back on you? We scraped the profiles of about one hundred people on a social network and had Turkers guess those people’s traits – including age, ethnicity, intelligence, political affiliation, and intoxication. To be fair, we included pictures of ourselves in the batch.

You can see the photos ordered on three axes here:

More details after the jump:

There was mostly consensus from Turkers on guessing ethnicity. Next time we might compare Turker opinion against the profiles’ self-reported ethnicity.

The median error on guessing age was 2 years too young – which makes sense, as the photos were likely taken a year or two ago. Some of the larger negative errors appear to be on photos of 30- and 40-year-olds from high school. Here is a histogram for the error on the guesses:

Unsurprisingly, perceived intelligence correlates with perceived age.

As you can tell we are mostly having fun with this set of questions, but we have some good ideas on how to refine the experiment. If you’re interested in being analyzed in the next round, join the Dolores Labs Facebook group.

Look for some intoxication analysis in the next post.

- Mike