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      Using Facebook Ads Audiences for Global Lifestyle Disease Surveillance: Promises and Limitations

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          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.

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          Most cited references6

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          Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study

          The Lancet, 349(9064), 1498-1504
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            Social media in public health.

            While social media interactions are currently not fully understood, as individual health behaviors and outcomes are shared online, social media offers an increasingly clear picture of the dynamics of these processes.
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              Adaptive nowcasting of influenza outbreaks using Google searches

              Seasonal influenza outbreaks and pandemics of new strains of the influenza virus affect humans around the globe. However, traditional systems for measuring the spread of flu infections deliver results with one or two weeks delay. Recent research suggests that data on queries made to the search engine Google can be used to address this problem, providing real-time estimates of levels of influenza-like illness in a population. Others have however argued that equally good estimates of current flu levels can be forecast using historic flu measurements. Here, we build dynamic ‘nowcasting’ models; in other words, forecasting models that estimate current levels of influenza, before the release of official data one week later. We find that when using Google Flu Trends data in combination with historic flu levels, the mean absolute error (MAE) of in-sample ‘nowcasts’ can be significantly reduced by 14.4%, compared with a baseline model that uses historic data on flu levels only. We further demonstrate that the MAE of out-of-sample nowcasts can also be significantly reduced by between 16.0% and 52.7%, depending on the length of the sliding training interval. We conclude that, using adaptive models, Google Flu Trends data can indeed be used to improve real-time influenza monitoring, even when official reports of flu infections are available with only one week's delay.
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                Author and article information

                Journal
                2017-05-11
                Article
                1705.04045
                6917e080-e28f-4735-ba31-15140ee99d3a

                http://creativecommons.org/licenses/by-nc-sa/4.0/

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                Custom metadata
                Please cite the article published at WebSci'17 instead of this arxiv version
                cs.CY

                Applied computer science
                Applied computer science

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