© 2018 Damon Crockett.
A small multiples (or faceted) plot showing the relationship between correlation strength (ρ) and p-value for pairs of variables in our data. Relationships between numbers of faces in images and housing, income, and education are particularly strong. Red dots represent visual variables whose correlation with the indicated socioeconomic variable has a family-wise error rate of less than 0.00017. Across housing, income, and education, “face present”—meaning the image contains at least one face—is an excellent predictor.

Socioeconomics and Tweeted Images

Traditional methods of recording socio-economic information about populations, such as censuses and surveys, have poor temporal resolution and are costly to conduct. More recently, computational models that use text features from online social network posts can predict several key socioeconomic variables at high accuracies and bypass the aforementioned limitations. However, even these models so far only use text (such as tweets), ignoring another key type of social media: images. In this project, we explore features from visual social media to develop computational models that estimate several socio-economic characteristics. We extract simple features, such as color histograms and number of faces, from over 7 million images posted on Twitter in 2013 across 60 U.S. cities. We find that aggregated characteristics of these images can be used to accurately predict income, housing prices, education levels, and financial well-being indicators. Our results suggest that images shared on online social networks reflect socio-economic characteristics and that this data can compliment existing computational models that use only text.