Using Facebook Ads audiences for global lifestyle disease surveillance: Promises and limitations

Matheus Araújo, Yelena Mejova, Ingmar Weber, Fabrício Benevenuto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

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 find that, for modeling prevalence of health conditions across countries, differences 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.

Original languageEnglish
Title of host publicationWebSci 2017 - Proceedings of the 2017 ACM Web Science Conference
PublisherAssociation for Computing Machinery, Inc
Pages253-257
Number of pages5
ISBN (Electronic)9781450348966
DOIs
Publication statusPublished - 25 Jun 2017
Event9th ACM Web Science Conference, WebSci 2017 - Troy, United States
Duration: 25 Jun 201728 Jun 2017

Other

Other9th ACM Web Science Conference, WebSci 2017
CountryUnited States
CityTroy
Period25/6/1728/6/17

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Keywords

  • Advertising
  • Epidemiology
  • Facebook
  • Health
  • Social media

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Araújo, M., Mejova, Y., Weber, I., & Benevenuto, F. (2017). Using Facebook Ads audiences for global lifestyle disease surveillance: Promises and limitations. In WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference (pp. 253-257). Association for Computing Machinery, Inc. https://doi.org/10.1145/3091478.3091513