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.
In Social media in public health'' Kass-Hout & Alhinnawi assert, Social media can provide timely, relevant, and transparent information of public health importance'' @cite_14 , formulating an exciting ongoing research to study health trends via online data. Following this call for action, some researchers focus on information seeking on topics such as abortion @cite_7 and vaccines @cite_13 , others use the data to now-cast diseases @cite_8 , while others use search logs, most notably using Google Trends, to predict seasonal flu @cite_17 .
@cite_14 @cite_7 @cite_8 @cite_13 @cite_17
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Crowdsourcing, folksonomy, user-generated content, social networks, the sharing economy, peer production, Multi-User Virtual Environments, participatory media, collaborative creativity and Big Data (see Table 1) are all terms in the expanding lexicon of user-contributed data and user-led innovation (Sharp, Darren and Salomon, Mandy, User-led innovation: a new framework for co-creating business and social value, Smart Internet Background: The authors present a case study of a public health campaign, including social media, and aiming at maximizing the use of web app on sexual health. Objective: To analyze the impact of a Facebook fan page, Facebook advertisements, and posters to maximize the number of visits to the educational web app. Methods: The campaign is assessed for 1 year, using data tracked through Facebook statistics and Google Analytics. Results: The site had 3670 visits 10.1 visitors day, 95 CI 8.7-11.4. During the one-month Facebook Ads campaign, the site received 1263 visits 42.1 visitors day, 95 CI 37.3-46.9, multiplying by over four the average number of visitors day. 34.4 of all the participants were recruited during the one-month Facebook ads campaign. Conclusions: Facebook advertisements seem to be a good tool to promote an educational web app on sexual health targeting youth, and to reach a huge number of users rapidly and at a low cost. Twitter is a unique social media channel, in the sense that users discuss and talk about the most diverse topics, including their health conditions. In this paper we analyze how Dengue epidemic is reflected on Twitter and to what extent that information can be used for the sake of surveillance. Dengue is a mosquito-borne infectious disease that is a leading cause of illness and death in tropical and subtropical regions, including Brazil. We propose an active surveillance methodology that is based on four dimensions: volume, location, time and public perception. First we explore the public perception dimension by performing sentiment analysis. This analysis enables us to filter out content that is not relevant for the sake of Dengue surveillance. Then, we verify the high correlation between the number of cases reported by official statistics and the number of tweets posted during the same time period (i.e., R2 = 0.9578). A clustering approach was used in order to exploit the spatio-temporal dimension, and the quality of the clusters obtained becomes evident when they are compared to official data (i.e., RandIndex = 0.8914). As an application, we propose a Dengue surveillance system that shows the evolution of the dengue situation reported in tweets, which is implemented in www.observatorio.inweb.org.br dengue . Vaccination campaigns are one of the most important and successful public health programs ever undertaken. People who want to learn about vaccines in order to make an informed decision on whether to vaccinate are faced with a wealth of information on the Internet, both for and against vaccinations. In this paper we develop an automated way to score Internet search queries and web pages as to the likelihood that a person making these queries or reading those pages would decide to vaccinate. We apply this method to data from a major Internet search engine, while people seek information about the Measles, Mumps and Rubella (MMR) vaccine. We show that our method is accurate, and use it to learn about the information acquisition process of people. Our results show that people who are pro-vaccination as well as people who are anti-vaccination seek similar information, but browsing this information has differing effect on their future browsing. These findings demonstrate the need for health authorities to tailor their information according to the current stance of users.