Venue: AS7 06-42 FASS Research Division Seminar Room, The Shaw Foundation Building, 5 Arts Link, Singapore 117570

Facebook, Google, TikTok & Co. generate their revenue from advertising. To offer advertisers with targeting capabilities, these companies collect large amounts of user data to build elaborate profiles. Based on these profiles an advertiser can then choose to target only, say, female Facebook users living in Norte de Santander, Colombia, who are aged 18-24, who used to live in Venezuela, and who have access to an iOS device. To help advertisers in planning their advertising campaigns and the related budget needs, the advertising platforms provide so-called audience estimates on how many of their users match the provided targeting criteria. In the example above, Facebook estimates that there are 1,800 matching users. – I’ll describe how, we’re tapping into these audience estimates to (i) monitor international migration, (ii) track digital gender gaps, and (iii) map wealth inequalities. We consistently find that, despite fake profiles, sampling bias, and noise in the inference algorithms, data derived from the advertising platforms provides valuable information that is complementary to other data sources. At the same time, our work shows the risk of identifying vulnerable groups, rather than individuals, which is often not adequately considered in discussions focused on individual privacy. – At the end of the presentation I’ll give a quick overview of other data sources we’re using for Societal Computing, including data donations, satellite imagery, reviews from Google Maps, and more.