According to a new study, offline social networks, revealed by co-location data, predict U.S. voting patterns more accurately than online social connections or residential sorting. Michele Tizzoni and colleagues analyzed large-scale data on co-location patterns from Meta’s Data for Good program, which collates anonymized data collected from people who enabled location services on the Facebook smartphone app. Their results are published in PNAS Nexus.
Colocation is defined as two people being within the same map tile, which is less than 600×600 meters, depending on latitude. The political affiliation of each person was inferred from their county of residence.
This data was compared with Facebook friendships and residential proximity for all U.S. counties, along with individual survey responses from 2,420 Americans regarding their offline and online social networks during the 2020 presidential election. For the residential proximity measurement, the voter registrations of the closest 1,000 neighbors were used.