Introduced by Souhail Bentaleb, Guillaume Drugeot, Luna Izpisua Rodriguez, Farbod Nowzad and Ajay Shah
Using data science and machine learning, a team of UC Berkeley students have created a program that can tell an accurate and informative story about a person’s life — all from the photos that they post on social media.
Typically, advertisers target consumers by analyzing a person’s most frequented online sites or “clicks” to collect basic demographic information, such as age, gender and location. But they’re overlooking an entire other aspect of people’s online lives, which could be a rich source consumer information: personal photos.
The 1.8 billion images that are being posted to Facebook, Instagram, Flickr, Snapchat and WhatsApp every day hold valuable information about a person’s lifestyle, daily activities and consumer behaviors .
In order to address this problem, the team of students created a machine learning algorithm that tags photos from social media profiles to reveal the most prominent keywords that define each image. From there, the algorithm uses this information to classify each consumer profile into one of four categories: outdoorsy, sporty, family or foodie.
This classification can provide advertisers with personalized insights about a consumer’s preferences.
The general public can use the tool as well in order to gain insight into the image that they project online.