Through special custom software we collected data from more than 1,000,000 Facebook users. What we collected is their "public data" - some of their personal data (name, country, Facebook groups they subscribe to) plus their main profile picture and a few friend relationships. We built a database with all this data, then began to analyze the pictures that showed smiling faces. The vast majority of pictures were both amateurish and somehow almost involuntarily or unconsciously alluring. And they are almost always "smiling".
It's also evident that the majority of users want to appear in the best shape and look. They are acting on Facebook’s mandatory mechanism: establish new relationships. Facebook is based on the voluntary uploading of personal data and sharing it with friends. The more friends the better. Being personal and popular a Facebook user is exposing him/herself to many others, continuing to establish new relationships.
Once the database was ready, we studied and customized a face recognition algorithm. The algorithm used self learning neural networks and was programmed to "group" the huge amount of faces we collected (and their attached data) in a few simple categories. The categories are among the most popular that we usually use to define a person at a distance, without knowing him/her, or judging based only on a few behaviors. We picked six categories ("climber", "easy going", "funny", "mild", "sly" and "smug" - working definitions), with some intuitive differences, for both male and female subjects.
The software effectively extracted 250,000 faces that were connected to the relevant public data in our database.
After grouping them, we started to dive into these seas of faces, with all the perceptual consequences. And we started to think about why we felt so overwhelmed.