Using Facebook Ads Audiences for Global Lifestyle Disease Surveillance

Abstract

Every day, millions of users reveal their interests on Facebook, which are then monetized via targeted advertisement marketing campaigns. In this paper, we explore the use of demographically rich Facebook Ads audience estimates for tracking non-communicable diseases around the world. Across 47 countries, we compute the audiences of marker interests, and evaluate their potential in tracking health conditions associated with tobacco use, obesity, and diabetes, compared to the performance of placebo interests. Despite its huge potential, we nd that, for modeling prevalence of health conditions across countries, di erences in these interest audiences are only weakly indicative of the corresponding prevalence rates. Within the countries, however, our approach provides interesting insights on trends of health awareness across demographic groups. Finally, we provide a temporal error analysis to expose the potential pitfalls of using Facebook's Marketing API as a black box.Please cite the article published at WebSci'17 instead of this arxiv version.

Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide

“…Seminal works using this approach focused on the health domain [31,39]. For example, Araújo et al [3] extracted data in 47 countries to track health conditions associated with lifestyle diseases. They showed that, within each country, Facebook data could provide insights into different trends of health awareness across demographic groups.…”

The Impact of Hurricane Maria on Out‐migration from Puerto Rico: Evidence from Facebook Data

“…Information is extracted from the API programmatically using the language Python, and in particular, the module pySocialWatcher (Araujo et al. ).…”

“…We use the Ads Manager application, Facebook's Marketing Application Programming Interface (API), to extract estimates of potential reach over time. Information is extracted from the API programmatically using the language Python, and in particular, the module pySocialWatcher (Araujo et al 2017).…”

Promises and Pitfalls of Using Digital Traces for Demographic Research

“…For example, Araújo et al (2017) tracked the size of audiences in Facebook with interests that could be markers of tobacco use, obesity, or diabetes. Although their results were negative—meaning that they found that differences in interest audiences were only weakly indicative of the corresponding prevalence rates—they developed an analysis approach that can be used in other contexts.…”

“…The premise of creating a baseline was to control for the amount of time and number of searches people generally conduct on Facebook. In other words, Araújo et al (2017) used a form of normalization for behavioral features of Facebook users. Developing tools to standardize compositional changes in the population of Facebook users is another area where demographers can develop methodological advances.…”