{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T15:32:56Z","timestamp":1775835176962,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T00:00:00Z","timestamp":1595980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T00:00:00Z","timestamp":1595980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Qatar National Library","award":["Open Access Fund"],"award-info":[{"award-number":["Open Access Fund"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EPJ Data Sci."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The United Nations Sustainable Development Goals (SDGs) are a global consensus on the world\u2019s 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\u2019s 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 However, existing data sources to measure progress on the SDGs are often outdated or lacking appropriate disaggregation.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.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-020-00235-w","type":"journal-article","created":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T08:07:51Z","timestamp":1596010071000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Mapping socioeconomic indicators using social media advertising data"],"prefix":"10.1140","volume":"9","author":[{"given":"Masoomali","family":"Fatehkia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isabelle","family":"Tingzon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ardie","family":"Orden","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephanie","family":"Sy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vedran","family":"Sekara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manuel","family":"Garcia-Herranz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4169-2579","authenticated-orcid":false,"given":"Ingmar","family":"Weber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,29]]},"reference":[{"key":"235_CR1","unstructured":"United Nations: (2015) Transforming our World: The 2030 Agenda for Sustainable Development. Technical report. https:\/\/sustainabledevelopment.un.org\/post2015\/transformingourworld\/publication. Accessed 2019-09-29"},{"key":"235_CR2","unstructured":"World Bank (2019) PovcalNet. http:\/\/iresearch.worldbank.org\/PovcalNet\/povOnDemand.aspx. Accessed 2019-09-29"},{"key":"235_CR3","unstructured":"Open data Watch: (2019) Bridging Gender Data Gaps in Africa. Technical report. https:\/\/opendatawatch.com\/publications\/bridging-gender-data-gaps-in-africa\/. Accessed 2019-09-29"},{"issue":"6301","key":"235_CR4","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1126\/science.aah5217","volume":"353","author":"JE Blumenstock","year":"2016","unstructured":"Blumenstock JE (2016) Fighting poverty with data. Science 353(6301):753\u2013754. https:\/\/doi.org\/10.1126\/science.aah5217. Accessed 2019-06-25","journal-title":"Science"},{"issue":"12","key":"235_CR5","doi-asserted-by":"publisher","first-page":"4988","DOI":"10.3390\/su5124988","volume":"5","author":"T Ghosh","year":"2013","unstructured":"Ghosh T, Anderson SJ, Elvidge CD, Sutton PC (2013) Using nighttime satellite imagery as a proxy measure of human well-being. Sustainability 5(12):4988\u20135019. https:\/\/doi.org\/10.3390\/su5124988. Accessed 2019-05-01","journal-title":"Sustainability"},{"issue":"8","key":"235_CR6","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1016\/j.cageo.2009.01.009","volume":"35","author":"CD Elvidge","year":"2009","unstructured":"Elvidge CD, Sutton PC, Ghosh T, Tuttle BT, Baugh KE, Bhaduri B, Bright E (2009) A global poverty map derived from satellite data. Comput Geosci 35(8):1652\u20131660. https:\/\/doi.org\/10.1016\/j.cageo.2009.01.009. Accessed 2019-06-25","journal-title":"Comput Geosci"},{"key":"235_CR7","doi-asserted-by":"publisher","unstructured":"Pinkovskiy M, Sala-i-Martin X (2014) Lights, camera, \u2026 income!: estimating poverty using national accounts, survey means, and lights. Working Paper 19831, National Bureau of Economic Research. https:\/\/doi.org\/10.3386\/w19831. http:\/\/www.nber.org\/papers\/w19831. Accessed 2019-05-01","DOI":"10.3386\/w19831"},{"issue":"1","key":"235_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/1478-7954-6-5","volume":"6","author":"AM Noor","year":"2008","unstructured":"Noor AM, Alegana VA, Gething PW, Tatem AJ, Snow RW (2008) Using remotely sensed night-time light as a proxy for poverty in Africa. Popul Health Metr 6(1):5. https:\/\/doi.org\/10.1186\/1478-7954-6-5. Accessed 2019-06-25","journal-title":"Popul Health Metr"},{"issue":"8","key":"235_CR9","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1016\/j.asr.2012.01.025","volume":"49","author":"W Wang","year":"2012","unstructured":"Wang W, Cheng H, Zhang L (2012) Poverty assessment using DMSP\/OLS night-time light satellite imagery at a provincial scale in China. Adv Space Res 49(8):1253\u20131264. https:\/\/doi.org\/10.1016\/j.asr.2012.01.025. Accessed 2019-06-25","journal-title":"Adv Space Res"},{"issue":"10","key":"235_CR10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0139779","volume":"10","author":"C Mellander","year":"2015","unstructured":"Mellander C, Lobo J, Stolarick K, Matheson Z (2015) Night-time light data: a good proxy measure for economic activity?. PLoS ONE 10(10):0139779. https:\/\/doi.org\/10.1371\/journal.pone.0139779. Accessed 2019-06-25","journal-title":"PLoS ONE"},{"issue":"21","key":"235_CR11","doi-asserted-by":"publisher","first-page":"8589","DOI":"10.1073\/pnas.1017031108","volume":"108","author":"X Chen","year":"2011","unstructured":"Chen X, Nordhaus WD (2011) Using luminosity data as a proxy for economic statistics. Proc Natl Acad Sci 108(21):8589\u20138594. https:\/\/doi.org\/10.1073\/pnas.1017031108. Accessed 2019-05-01","journal-title":"Proc Natl Acad Sci"},{"issue":"2","key":"235_CR12","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1257\/aer.102.2.994","volume":"102","author":"JV Henderson","year":"2012","unstructured":"Henderson JV, Storeygard A, Weil DN (2012) Measuring economic growth from outer space. Am Econ Rev 102(2):994\u20131028. https:\/\/doi.org\/10.1257\/aer.102.2.994. Accessed 2019-06-25","journal-title":"Am Econ Rev"},{"issue":"6301","key":"235_CR13","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1126\/science.aaf7894","volume":"353","author":"N Jean","year":"2016","unstructured":"Jean N, Burke M, Xie M, Davis WM, Lobell DB, Ermon S (2016) Combining satellite imagery and machine learning to predict poverty. Science 353(6301):790\u2013794. https:\/\/doi.org\/10.1126\/science.aaf7894. Accessed 2019-02-03","journal-title":"Science"},{"key":"235_CR14","doi-asserted-by":"crossref","unstructured":"Engstrom R, Hersh JS, Newhouse DL (2017) Poverty from space: using high-resolution satellite imagery for estimating economic well-being. Technical Report WPS8284, The World Bank. http:\/\/documents.worldbank.org\/curated\/en\/610771513691888412\/Poverty-from-space-using-high-resolution-satellite-imagery-for-estimating-economic-well-being. Accessed 2019-05-01","DOI":"10.1596\/1813-9450-8284"},{"key":"235_CR15","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1145\/3136560.3136576","volume-title":"Proceedings of the ninth international conference on information and communication technologies and development. ICTD \u201917","author":"A Head","year":"2017","unstructured":"Head A, Manguin M, Tran N, Blumenstock JE (2017) Can human development be measured with satellite imagery? In: Proceedings of the ninth international conference on information and communication technologies and development. ICTD \u201917. ACM, New York, pp 8\u20131811. https:\/\/doi.org\/10.1145\/3136560.3136576. event-place: Lahore, Pakistan. Accessed 2019-05-01"},{"issue":"4","key":"235_CR16","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1073\/pnas.1812969116","volume":"116","author":"GR Watmough","year":"2019","unstructured":"Watmough GR, Marcinko CLJ, Sullivan C, Tschirhart K, Mutuo PK, Palm CA, Svenning J-C (2019) Socioecologically informed use of remote sensing data to predict rural household poverty. Proc Natl Acad Sci 116(4):1213\u20131218. https:\/\/doi.org\/10.1073\/pnas.1812969116. Accessed 2019-05-01","journal-title":"Proc Natl Acad Sci"},{"key":"235_CR17","series-title":"Lecture notes in computer science","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/978-3-642-22362-4_35","volume-title":"User modeling, adaption and personalization","author":"V Soto","year":"2011","unstructured":"Soto V, Frias-Martinez V, Virseda J, Frias-Martinez E (2011) Prediction of socioeconomic levels using cell phone records. In: Konstan JA, Conejo R, Marzo JL, Oliver N (eds) User modeling, adaption and personalization. Lecture notes in computer science. Springer, Berlin, pp 377\u2013388"},{"key":"235_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3220547.3220549","volume-title":"Proceedings of the fourth international workshop on data science for macro-modeling with financial and economic datasets. DSMM\u201918","author":"L Fernando","year":"2018","unstructured":"Fernando L, Surendra A, Lokanathan S, Gomez T (2018) Predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka. In: Proceedings of the fourth international workshop on data science for macro-modeling with financial and economic datasets. DSMM\u201918. ACM, New York, pp 1\u20131112. https:\/\/doi.org\/10.1145\/3220547.3220549. event-place: Houston, TX, USA. Accessed 2019-05-01"},{"key":"235_CR19","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.jbusres.2016.08.005","volume":"70","author":"C Njuguna","year":"2017","unstructured":"Njuguna C, McSharry P (2017) Constructing spatiotemporal poverty indices from big data. J Bus Res 70:318\u2013327. https:\/\/doi.org\/10.1016\/j.jbusres.2016.08.005. Accessed 2019-06-25","journal-title":"J Bus Res"},{"key":"235_CR20","doi-asserted-by":"publisher","unstructured":"Hernandez M, Hong L, Frias-Martinez V, Frias-Martinez E (2017) Estimating poverty using cell phone data: evidence from Guatemala. Technical report, The World Bank. https:\/\/doi.org\/10.1596\/1813-9450-7969. Accessed 2019-06-25","DOI":"10.1596\/1813-9450-7969"},{"issue":"6264","key":"235_CR21","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1126\/science.aac4420","volume":"350","author":"J Blumenstock","year":"2015","unstructured":"Blumenstock J, Cadamuro G, On R (2015) Predicting poverty and wealth from mobile phone metadata. Science 350(6264):1073\u20131076. https:\/\/doi.org\/10.1126\/science.aac4420. Accessed 2019-02-05","journal-title":"Science"},{"issue":"46","key":"235_CR22","doi-asserted-by":"publisher","first-page":"9783","DOI":"10.1073\/pnas.1700319114","volume":"114","author":"N Pokhriyal","year":"2017","unstructured":"Pokhriyal N, Jacques DC (2017) Combining disparate data sources for improved poverty prediction and mapping. Proc Natl Acad Sci 114(46):9783\u20139792. https:\/\/doi.org\/10.1073\/pnas.1700319114. Accessed 2019-05-01","journal-title":"Proc Natl Acad Sci"},{"issue":"127","key":"235_CR23","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2016.0690","volume":"14","author":"JE Steele","year":"2017","unstructured":"Steele JE, Sunds\u00f8y PR, Pezzulo C, Alegana VA, Bird TJ, Blumenstock J, Bjelland J, Eng\u00f8-Monsen K, de Montjoye YA, Iqbal AM, Hadiuzzaman KN, Lu X, Wetter E, Tatem AJ, Bengtsson L (2017) Mapping poverty using mobile phone and satellite data. J R Soc Interface 14(127):20160690. https:\/\/doi.org\/10.1098\/rsif.2016.0690. Accessed 2019-05-01","journal-title":"J R Soc Interface"},{"key":"235_CR24","doi-asserted-by":"publisher","first-page":"425","DOI":"10.5194\/isprs-archives-XLII-4-W19-425-2019","volume-title":"ISPRS\u2014international archives of the photogrammetry, remote sensing and spatial information sciences XLII-4\/W19","author":"I Tingzon","year":"2019","unstructured":"Tingzon I, Orden A, Go KT, Sy S, Sekara V, Weber I, Fatehkia M, Garc\u00eda-Herranz M, Kim D (2019) Mapping poverty in the Philippines using machine learning, satellite imagery, and crowd-sourced geospatial information. In: ISPRS\u2014international archives of the photogrammetry, remote sensing and spatial information sciences XLII-4\/W19, pp 425\u2013431. https:\/\/doi.org\/10.5194\/isprs-archives-XLII-4-W19-425-2019"},{"issue":"4","key":"235_CR25","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1111\/padr.12102","volume":"43","author":"E Zagheni","year":"2017","unstructured":"Zagheni E, Weber I, Gummadi K (2017) Leveraging Facebook\u2019s advertising platform to monitor stocks of migrants. Popul Dev Rev 43(4):721\u2013734. https:\/\/doi.org\/10.1111\/padr.12102. Accessed 2019-02-03","journal-title":"Popul Dev Rev"},{"issue":"10","key":"235_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0224134","volume":"14","author":"S Spyratos","year":"2019","unstructured":"Spyratos S, Vespe M, Natale F, Weber I, Zagheni E, Rango M (2019) Quantifying international human mobility patterns using Facebook network data. PLoS ONE 14(10):1\u201322. https:\/\/doi.org\/10.1371\/journal.pone.0224134","journal-title":"PLoS ONE"},{"issue":"27","key":"235_CR27","doi-asserted-by":"publisher","first-page":"6958","DOI":"10.1073\/pnas.1717781115","volume":"115","author":"D Garcia","year":"2018","unstructured":"Garcia D, Kassa YM, Cuevas A, Cebrian M, Moro E, Rahwan I, Cuevas R (2018) Analyzing gender inequality through large-scale Facebook advertising data. Proc Natl Acad Sci 115(27):6958\u20136963. https:\/\/doi.org\/10.1073\/pnas.1717781115. Accessed 2019-02-05","journal-title":"Proc Natl Acad Sci"},{"key":"235_CR28","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.worlddev.2018.03.007","volume":"107","author":"M Fatehkia","year":"2018","unstructured":"Fatehkia M, Kashyap R, Weber I (2018) Using Facebook ad data to track the global digital gender gap. World Dev 107:189\u2013209. https:\/\/doi.org\/10.1016\/j.worlddev.2018.03.007. Accessed 2019-02-03","journal-title":"World Dev"},{"key":"235_CR29","unstructured":"Pew Research Center (2019) Mobile connectivity in emerging economies. Technical report. https:\/\/www.pewinternet.org\/2019\/03\/07\/mobile-connectivity-in-emerging-economies\/. Accessed 2019-06-20"},{"key":"235_CR30","volume-title":"The DHS Wealth Index","author":"SO Rutstein","year":"2004","unstructured":"Rutstein SO, Johnson K (2004) The DHS Wealth Index, ORC Macro, Calverton. http:\/\/dhsprogram.com\/pubs\/pdf\/CR6\/CR6.pdf"},{"key":"235_CR31","unstructured":"Philippine Statistics Authority, ICF (2018) The DHS Program\u2014Philippines: Standard DHS, 2017 [Dataset] Quezon City, Philippines, and Rockville, Maryland, USA. https:\/\/dhsprogram.com\/what-we-do\/survey\/survey-display-510.cfm. Accessed 2019-06-20"},{"key":"235_CR32","unstructured":"International Institute for Population Sciences\u2014IIPS\/India and ICF (2017) The DHS Program\u2014India: Standard DHS, 2015-16 [Dataset] Mumbai, India: IIPS and ICF. https:\/\/dhsprogram.com\/what-we-do\/survey\/survey-display-355.cfm. Accessed 2019-09-04"},{"key":"235_CR33","unstructured":"School of Geography and Environmental Science, University of Southampton, Department of Geography and Geosciences, University of Louisville, Departement de Geographie, Universite de Namur, Center for International Earth Science Information Network (CIESIN), Columbia University (2018) WorldPop\u2014global high resolution population denominators project. https:\/\/www.worldpop.org\/. Accessed 2019-06-03"},{"issue":"2","key":"235_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0107042","volume":"10","author":"FR Stevens","year":"2015","unstructured":"Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLoS ONE 10(2):1\u201322. https:\/\/doi.org\/10.1371\/journal.pone.0107042","journal-title":"PLoS ONE"},{"key":"235_CR35","unstructured":"Nicas J (2019) Does Facebook really know how many fake. Accounts it has? The New York times. Chap. Technology. Accessed 2020-04-16"},{"key":"235_CR36","volume-title":"The Web Conference (WWW)","author":"D Rama","year":"2020","unstructured":"Rama D, Mejova Y, Tizzoni M, Kalimeri K, Weber I (2020) Facebook Ads as a demographic tool to measure the urban-rural divide. In: The Web Conference (WWW)"},{"key":"235_CR37","volume-title":"ACM web science","author":"M Araujo","year":"2017","unstructured":"Araujo M, Mejova Y, Weber I, Benevenuto F (2017) Using Facebook ads audiences for global lifestyle disease surveillance: promises and limitations. In: ACM web science. ACM, New York"},{"issue":"2","key":"235_CR38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0055882","volume":"8","author":"AE Gaughan","year":"2013","unstructured":"Gaughan AE, Stevens FR, Linard C, Jia P, Tatem AJ (2013) High resolution population distribution maps for southeast Asia in 2010 and 2015. PLoS ONE 8(2):55882. https:\/\/doi.org\/10.1371\/journal.pone.0055882. Accessed 2019-06-03","journal-title":"PLoS ONE"},{"issue":"1","key":"235_CR39","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc, Ser B, Methodol 58(1):267\u2013288. Accessed 2018-07-30","journal-title":"J R Stat Soc, Ser B, Methodol"},{"key":"235_CR40","volume-title":"The elements of statistical learning\u2014data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning\u2014data mining, inference, and prediction, 2nd edn. https:\/\/www.springer.com\/gp\/book\/9780387848570. Accessed 2019-09-29","edition":"2"},{"key":"235_CR41","unstructured":"Gething P, Tatem A, Bird T, Burgert-Brucker CR (2015) Creating spatial interpolation surfaces with DHS data. Technical report. https:\/\/dhsprogram.com\/publications\/publication-SAR11-Spatial-Analysis-Reports.cfm. Accessed 2019-06-20"},{"key":"235_CR42","unstructured":"UN General Assembly (2015) Transforming our world: the 2030 Agenda for Sustainable Development. Technical report. https:\/\/sustainabledevelopment.un.org\/post2015\/transformingourworld\/publication. Accessed 2019-06-27"},{"key":"235_CR43","unstructured":"International Bank for Reconstruction and Development\/The World Bank (2018) Poverty and Shared Prosperity 2018: Piecing together the poverty puzzle Washington DC, USA. https:\/\/www.worldbank.org\/en\/publication\/poverty-and-shared-prosperity. Accessed 2019-09-22"},{"key":"235_CR44","doi-asserted-by":"crossref","unstructured":"Munoz Boudet AM, Buitrago P, Leroy de la Briere B, Newhouse D, Rubiano Matulevich E, Scott K, Suarrez-Becerra P (2018) Gender differences in poverty and household composition through the life-cycle: a global perspective. Technical Report WPS8360, World Bank Group, Washington DC. http:\/\/documents.worldbank.org\/curated\/en\/135731520343670750\/Gender-differences-in-poverty-and-household-composition-through-the-life-cycle-a-global-perspective. Accessed 2019-09-22","DOI":"10.1596\/1813-9450-8360"},{"key":"235_CR45","unstructured":"World Economic Forum (2018). Global Gender Gap Report. Technical report, World Economic Forum (2018). wef.ch\/gggr18. Accessed 2019-06-26"},{"key":"235_CR46","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-319-13734-6_9","volume-title":"Social informatics","author":"G Magno","year":"2014","unstructured":"Magno G, Weber I (2014) International gender differences and gaps in online social networks. In: Social informatics, pp 121\u2013138. https:\/\/doi.org\/10.1007\/978-3-319-13734-6_9"}],"container-title":["EPJ Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-020-00235-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1140\/epjds\/s13688-020-00235-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-020-00235-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T23:41:39Z","timestamp":1723333299000},"score":1,"resource":{"primary":{"URL":"https:\/\/epjdatascience.springeropen.com\/articles\/10.1140\/epjds\/s13688-020-00235-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,29]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["235"],"URL":"https:\/\/doi.org\/10.1140\/epjds\/s13688-020-00235-w","relation":{},"ISSN":["2193-1127"],"issn-type":[{"value":"2193-1127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,29]]},"assertion":[{"value":"26 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22"}}