Mapping socioeconomic indicators using social media advertising data
Abstract The United Nations Sustainable Development Goals (SDGs) are a global consensus on the world’s most pressing challenges. They come with a set of 232 indicators against which countries should regularly monitor their progress, ensuring that everyone is represented in up-to-date data that can b...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-07-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1140/epjds/s13688-020-00235-w |
| _version_ | 1818254164783792128 |
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| author | Masoomali Fatehkia Isabelle Tingzon Ardie Orden Stephanie Sy Vedran Sekara Manuel Garcia-Herranz Ingmar Weber |
| author_facet | Masoomali Fatehkia Isabelle Tingzon Ardie Orden Stephanie Sy Vedran Sekara Manuel Garcia-Herranz Ingmar Weber |
| author_sort | Masoomali Fatehkia |
| collection | DOAJ |
| description | Abstract The United Nations Sustainable Development Goals (SDGs) are a global consensus on the world’s most pressing challenges. They come with a set of 232 indicators against which countries should regularly monitor their progress, ensuring that everyone is represented in up-to-date data that can be used to make decisions to improve people’s lives. However, existing data sources to measure progress on the SDGs are often outdated or lacking appropriate disaggregation. We evaluate the value that anonymous, publicly accessible advertising data from Facebook can provide in mapping socio-economic development in two low and middle income countries, the Philippines and India. Concretely, we show that audience estimates of how many Facebook users in a given location use particular device types, such as Android vs. iOS devices, or particular connection types, such as 2G vs. 4G, provide strong signals for modeling regional variation in the Wealth Index (WI), derived from the Demographic and Health Survey (DHS). We further show that, surprisingly, the predictive power of these digital connectivity features is roughly equal at both the high and low ends of the WI spectrum. Finally we show how such data can be used to create gender-disaggregated predictions, but that these predictions only appear plausible in contexts with gender equal Facebook usage, such as the Philippines, but not in contexts with large gender Facebook gaps, such as India. |
| first_indexed | 2024-12-12T16:51:37Z |
| format | Article |
| id | doaj.art-43159943c31a4e59aaccb857f658f9b2 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-12T16:51:37Z |
| publishDate | 2020-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-43159943c31a4e59aaccb857f658f9b22022-12-22T00:18:21ZengSpringerOpenEPJ Data Science2193-11272020-07-019111510.1140/epjds/s13688-020-00235-wMapping socioeconomic indicators using social media advertising dataMasoomali Fatehkia0Isabelle Tingzon1Ardie Orden2Stephanie Sy3Vedran Sekara4Manuel Garcia-Herranz5Ingmar Weber6Qatar Computing Research Institute, HBKUThinking MachinesThinking MachinesThinking MachinesUNICEF InnovationUNICEF InnovationQatar Computing Research Institute, HBKUAbstract The United Nations Sustainable Development Goals (SDGs) are a global consensus on the world’s most pressing challenges. They come with a set of 232 indicators against which countries should regularly monitor their progress, ensuring that everyone is represented in up-to-date data that can be used to make decisions to improve people’s lives. However, existing data sources to measure progress on the SDGs are often outdated or lacking appropriate disaggregation. We evaluate the value that anonymous, publicly accessible advertising data from Facebook can provide in mapping socio-economic development in two low and middle income countries, the Philippines and India. Concretely, we show that audience estimates of how many Facebook users in a given location use particular device types, such as Android vs. iOS devices, or particular connection types, such as 2G vs. 4G, provide strong signals for modeling regional variation in the Wealth Index (WI), derived from the Demographic and Health Survey (DHS). We further show that, surprisingly, the predictive power of these digital connectivity features is roughly equal at both the high and low ends of the WI spectrum. Finally we show how such data can be used to create gender-disaggregated predictions, but that these predictions only appear plausible in contexts with gender equal Facebook usage, such as the Philippines, but not in contexts with large gender Facebook gaps, such as India.http://link.springer.com/article/10.1140/epjds/s13688-020-00235-wPoverty mappingFacebook advertising dataRemote sensingGender data |
| spellingShingle | Masoomali Fatehkia Isabelle Tingzon Ardie Orden Stephanie Sy Vedran Sekara Manuel Garcia-Herranz Ingmar Weber Mapping socioeconomic indicators using social media advertising data EPJ Data Science Poverty mapping Facebook advertising data Remote sensing Gender data |
| title | Mapping socioeconomic indicators using social media advertising data |
| title_full | Mapping socioeconomic indicators using social media advertising data |
| title_fullStr | Mapping socioeconomic indicators using social media advertising data |
| title_full_unstemmed | Mapping socioeconomic indicators using social media advertising data |
| title_short | Mapping socioeconomic indicators using social media advertising data |
| title_sort | mapping socioeconomic indicators using social media advertising data |
| topic | Poverty mapping Facebook advertising data Remote sensing Gender data |
| url | http://link.springer.com/article/10.1140/epjds/s13688-020-00235-w |
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