In the global move toward urbanization, making sure the people remaining in rural areas are not left behind in terms of development and policy considerations is a priority for governments worldwide. However, it is increasingly challenging to track important statistics concerning this sparse, geographically dispersed population, resulting in a lack of reliable, up-to-date data. In this study, we examine the usefulness of the Facebook Advertising platform, which offers a digital "census" of over two billions of its users, in measuring potential rural-urban inequalities. We focus on Italy, a country where about 30% of the population lives in rural areas. First, we show that the population statistics that Facebook produces suffer from instability across time and incomplete coverage of sparsely populated municipalities. To overcome such limitation, we propose an alternative methodology for estimating Facebook Ads audiences that nearly triples the coverage of the rural municipalities from 19% to 55% and makes feasible fine-grained sub-population analysis. Using official national census data, we evaluate our approach and confirm known significant urban-rural divides in terms of educational attainment and income. Extending the analysis to Facebook-specific user "interests" and behaviors, we provide further insights on the divide, for instance, finding that rural areas show a higher interest in gambling. Notably, we find that the most predictive features of income in rural areas differ from those for urban centres, suggesting researchers need to consider a broader range of attributes when examining rural wellbeing. The findings of this study illustrate the necessity of improving existing tools and methodologies to include under-represented populations in digital demographic studies -- the failure to do so could result in misleading observations, conclusions, and most importantly, policies.
Social media in scientific research offer a unique digital observatory of human behaviours and hence great opportunities to conduct research at large scale answering complex sociodemographic questions. We focus on the identification and assessment of biases in social media administered surveys. This study aims to shed light on population, self-selection and behavioural biases, empirically comparing the consistency between self-reported information collected traditionally versus social media administered questionnaires, including demographic and psychometric attributes. We engaged a demographic…
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 …
All over the world, the population in rural and semi-urban areas is a major concern for policy makers as they require technology enablement most to get elevated from their living conditions. Financial Inclusion projects are meant to work in this area as well. However the reach of financial institutions in those areas is much lesser than in urban areas in general. New policies and strategies are being made towards making banking services more accessible to the people in rural and semi-urban area. Technology, in particular Internet of Things (IoT) and Mobility can play a major role in this conte…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；W.E. Upjohn Institute（W.E.厄普约翰研究所）
In this paper we introduce a framework for annotating a social media text corpora for various categories. Since, social media data is generated via individuals, it is important to annotate the text for the individuals demographic attributes to enable a socio-technical analysis of the corpora. Furthermore, when analyzing a large data-set we can often annotate a small sample of data and then train a prediction model using this sample to annotate the full data for the relevant categories. We use a case study of a Facebook comment corpora on student loan discussion which was annotated for gender, …
Monitoring advertising around controversial issues is an important step in ensuring accountability and transparency of political processes. To that end, we use the Facebook Ads Library to collect 2312 migration-related advertising campaigns in Italy over one year. Our pro- and anti-immigration classifier (F1=0.85) reveals a partisan divide among the major Italian political parties, with anti-immigration ads accounting for nearly 15M impressions. Although composing 47.6% of all migration-related ads, anti-immigration ones receive 65.2% of impressions. We estimate that about two thirds of all ca…
Targeted advertising on online social platforms has become increasingly relevant in the political marketing toolkit. Monitoring political advertising is crucial to ensure accountability and transparency of democratic processes. Leveraging Meta public library of sponsored content, we study the extent to which political ads were delivered on Facebook and Instagram in the run up to 2022 Italian general election. Analyzing over 23 k unique ads paid by 2.7 k unique sponsors, with an associated amount spent of 4 M EUR and over 1 billion views generated, we investigate temporal, geographical, and dem…
In many developing countries, timely and accurate information about birth rates and other demographic indicators is still lacking, especially for male fertility rates. Using anonymous and aggregate data from Facebook's Advertising Platform, we produce global estimates of the Mean Age at Childbearing (MAC), a key indicator of fertility postponement. Our analysis indicates that fertility measures based on Facebook data are highly correlated with conventional indicators based on traditional data, for those countries for which we have statistics. For instance, the correlation of the MAC computed u…
Targeted online advertising is on the forefront of political communication, allowing hyper-local advertising campaigns around elections and issues. In this study, we employ a new resource for political ad monitoring -- Facebook Ads Library -- to examine advertising concerning the issue of immigration in Italy. A crucial topic in Italian politics, it has recently been a focus of several populist movements, some of which have adopted social media as a powerful tool for voter engagement. Indeed, we find evidence of targeting by the parties both in terms of geography and demographics (age and gend…
As the rate of incarceration in the United States continues to grow, a large body of research has been primarily focused on understanding the determinants and drivers of federal and state prison growth. However, local jail systems, with 11 million admissions each year, have generated less research attention even though they have a far broader impact on communities. Preliminary time trend analysis conducted by the Vera Institute of Justice (Vera) uncovered disparities in county jail incarceration rates by geography. Contrary to assumptions that incarceration is an urban phenomenon, Vera discove…
Political campaigns are increasingly turning to digital advertising to reach voters. These platforms empower advertisers to target messages to platform users with great precision, including through inferences about those users' political affiliations. However, prior work has shown that platforms' ad delivery algorithms can selectively deliver ads within these target audiences in ways that can lead to demographic skews along race and gender lines, often without an advertiser's knowledge. In this study, we investigate the impact of Facebook's ad delivery algorithms on political ads. We run a ser…
The enormous financial success of online advertising platforms is partially due to the precise targeting features they offer. Although researchers and journalists have found many ways that advertisers can target---or exclude---particular groups of users seeing their ads, comparatively little attention has been paid to the implications of the platform's ad delivery process, comprised of the platform's choices about which users see which ads. It has been hypothesized that this process can "skew" ad delivery in ways that the advertisers do not intend, making some users less likely than others to …
Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural India
Carnegie Mellon University（卡内基梅隆大学）；Indian Council for Research on International Economic Relations（印度国际经济关系研究委员会）；Institute for Human Development（人类发展研究所）；Indian Institute of Technology Delhi（印度理工学院德里分校）
A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery
Institute for Analytical Sociology, Linköping University, Sweden（分析社会研究所，利尔贝里大学，瑞典）；Geomatics, Department of Environmental and Life Sciences, Karlstad University, Sweden（测绘学，环境与生命科学系，卡尔斯塔德大学，瑞典）；Department of Statistics, Harvard University, Massachusetts, USA（统计学系，哈佛大学，马萨诸塞州，美国）；Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden（计算机科学与工程系，查尔姆斯理工大学，哥德堡，瑞典）；The AI and Global Development Lab (www.global-lab.ai)（人工智能与全球发展实验室）
One country, multiple portraits: representativeness in GPS-based mobility data is source-specific and spatially dependent
University of Liverpool（利物浦大学）；El Colegio de Mexico（墨西哥学院）；Central European University（中欧大学）；Center for Research and Advanced Studies of the National Polytechnic Institute, Irapuato Unit（墨西哥国立自治理工学院高级研究中心伊拉普阿托分部）；University College London（伦敦大学学院）
University of Chicago（芝加哥大学）；Lake Tanganyika Floating Health Clinic（坦噶尼喀湖浮动健康诊所）
Importance: Lower-resource areas in Africa and Asia face a unique set of healthcare challenges: the dual high burden of communicable and non-communicable diseases; a paucity of highly trained primary healthcare providers in both rural and densely populated urban areas; and a lack of reliable, inexpensive internet connections. Objective: To address these challenges, we designed an artificial intelligence assistant to help primary healthcare providers in lower-resource areas document demographic and medical sign/symptom data and to record and share diagnostic data in real-time with a centralized…
