Matheus Araujo
Yelena Mejova
Ingmar Weber
Fabricio Benevenuto
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.
- Jisun An and Ingmar Weber. 2015. Whom should we sense in "social sensing"- analyzing which users work best for social media now-casting. EPJ Data Science 4, 1 (2015), 1.Google Scholar
Cross Ref
- Matheus Araujo, Yelena Mejova, Michael Aupetit, and Ingmar Weber. 2017. Visualizing Health Awareness in the Middle East. In ICWSM. to appear.Google Scholar
- Yan Carriere-Swallow and Felipe Labbé. 2013. Nowcasting with Google Trends' in an emerging market. Journal of Forecasting 32, 4 (2013), 289--298.Google ScholarCross Ref
- Rumi Chunara, Lindsay Bouton, John W. Ayers, and John S. Brownstein. 2013. Assessing the Online Social Environment for Surveillance of Obesity Prevalence. PLoS ONE 8, 4 (04 2013), 1--8.Google Scholar
- Elia Gabarron, Luis Fernandez-Luque, and others. 2017. Impact of Facebook Ads for Sexual Health Promotion Via an Educational Web App: A Case Study. IJEHMC 8, 2 (2017), 18--32. Google ScholarDigital Library
- Jeremy Ginsberg and others. 2009. Detecting influenza epidemics using search engine query data. Nature 457, 7232 (2 2009), 1012--4.Google Scholar
- Steven Gittelman, Victor Lange, A. Carol Gotway Crawford, A. Catherine Okoro, Eugene Lieb, S. Satvinder Dhingra, and Elaine Trimarchi. 2015. A New Source of Data for Public Health Surveillance: Facebook Likes. JMIR 17, 4 (2015), e98.Google Scholar
- J. Gomide, A. Veloso, W. Meira Jr., V. Almeida, F. Benevenuto, F. Ferraz, and M. Teixeira. 2011. Dengue surveillance based on a computational model of spatio-temporal locality of Twitter. In WebSci. Google ScholarDigital Library
- L. Brianna Haskins and others. 2017. Health Evaluation and Referral Assistant: A Randomized Controlled Trial of a Web-Based Screening, Brief Intervention, and Referral to Treatment System to Reduce Risky Alcohol Use Among Emergency Department Patients. JMIR 19, 5 (May 2017), e119.Google Scholar
- Taha A. Kass-Hout and Hend Alhinnawi. 2013. Social media in public health. British Medical Bulletin 108, 1 (2013), 5--24.Google ScholarCross Ref
- Yoonsang Kim, Jidong Huang, and Sherry Emery. 2016. Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection. JMIR 18, 2 (26 Feb 2016), e41.Google Scholar
- David Lazer, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. The parable of Google flu: traps in big data analysis. Science 343, 6176 (2014).Google Scholar
- Yabing Liu, Chloe Kliman-Silver, Robert Bell, Balachander Krishnamurthy, and Alan Mislove. 2014. Measurement and analysis of osn ad auctions. In COSN. 139--150. Google ScholarDigital Library
- Christopher J.L. Murray and Alan D. Lopez. 1997. Alternative projections of mortality and disability by cause 1990--2020: Global Burden of Disease Study. Lancet 349 (1997), 1498--1504. Issue 9064.Google ScholarCross Ref
- Tobias Preis and Helen Susannah Moat. 2014. Adaptive nowcasting of influenza outbreaks using Google searches. Royal Society Open Science 1, 2 (2014).Google ScholarCross Ref
- Ben Y Reis and John S Brownstein. 2010. Measuring the impact of health policies using Internet search patterns: the case of abortion. BMC public health 10 (2010), 514.Google Scholar
- Diego Saez-Trumper, Yabing Liu, Ricardo Baeza-Yates, Balachander Krishnamurthy, and Alan Mislove. 2014. Beyond cpm and cpc: Determining the value of users on osns. In COSN. 161--168. Google ScholarDigital Library
- Koustuv Saha, Ingmar Weber, Michael L. Birnbaum, and Munmun De Choudhury. 2017. Characterizing Awareness of Schizophrenia Among Facebook Users by Leveraging Facebook Advertisement Estimates. JMIR 19, 5 (2017), e156.Google Scholar
- Elad Yom-Tov and Luis Fernandez-Luque. 2014. Information is in the eye of the beholder: Seeking information on the MMR vaccine through an Internet search engine. AMIA Symposium 2014 (2014), 1238--47.Google Scholar
Index Terms
- Using Facebook Ads Audiences for Global Lifestyle Disease Surveillance: Promises and Limitations
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