Özak, Ömer, Obradovich, Nick, Martín, Ignacio, Ortuño-Ortín, Ignacio, Awad, Edmond, Cebrián, Manuel, Cuevas, Rubén, Desmet, Klaus, Rahwan, Iyad, Cuevas, Ángel, Ministerio de Economía y Competitividad (España), Comunidad de Madrid, Ministerio de Educación, Cultura y Deporte (España), and European Commission
Culture has played a pivotal role in human evolution. Yet, the ability of social scientists to study culture is limited by the currently available measurement instruments. Scholars of culture must regularly choose between scalable but sparse survey-based methods or restricted but rich ethnographic methods. Here, we demonstrate that massive online social networks can advance the study of human culture by providing quantitative, scalable and high-resolution measurement of behaviourally revealed cultural values and preferences. We employ data across nearly 60 000 topic dimensions drawn from two billion Facebook users across 225 countries and territories. We first validate that cultural distances calculated from this measurement instrument correspond to traditional survey-based and objective measures of cross-national cultural differences. We then demonstrate that this expanded measure enables rich insight into the cultural landscape globally at previously impossible resolution. We analyse the importance of national borders in shaping culture and compare subnational divisiveness with gender divisiveness across countries. Our measure enables detailed investigation into the geopolitical stability of countries, social cleavages within small- and large-scale human groups, the integration of migrant populations and the disaffection of certain population groups from the political process, among myriad other potential future applications. Á.C. acknowledges funding from the European Union’s Horizon 2020 innovation action program under grant agreement no. 101019206 (TESTABLE project); and the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M (‘Fostering Young Doctors' Research’, UE-MEASURE-CM-UC3M). R.C. acknowledges funding from H2020 EU Project PIMCITY (grant no. 871370) and the Taptap Digital-UC3M Chair in Advanced AI and Data Science applied to Advertising and Marketing. Á.C., R.C, K.D, I.O.-O. and Ö.Ö. acknowledge funding from ECO2013-42710-P, MDM 2014-0431 and Fundacion BBVA. I.M. acknowledges funding from the Spanish Ministry of education with the FPU programme (FPU15/03518).