FORMATION CHARACTERISTICS OF DIGITAL DEMOGRAPHY IN BIG DATA ERA - Problems of Information Technology

FORMATION CHARACTERISTICS OF DIGITAL DEMOGRAPHY IN BIG DATA ERA - Problems of Information Technology

FORMATION CHARACTERISTICS OF DIGITAL DEMOGRAPHY IN BIG DATA ERA - Problems of Information Technology

FORMATION CHARACTERISTICS OF DIGITAL DEMOGRAPHY IN BIG DATA ERA - Problems of Information Technology

FORMATION CHARACTERISTICS OF DIGITAL DEMOGRAPHY IN BIG DATA ERA - Problems of Information Technology
FORMATION CHARACTERISTICS OF DIGITAL DEMOGRAPHY IN BIG DATA ERA - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№2, 2021

FORMATION CHARACTERISTICS OF DIGITAL DEMOGRAPHY IN BIG DATA ERA

Makrufa Sh. Hajirahimova, Aybaniz S. Aliyeva

The article is devoted to the formation of digital demography in the context of Big Data Revolution. The widespread use of the Internet and digital technologies has led to the emergence of a new direction in the science of demography, i.e., digital democracy. Digital Demography studies demographic processes by analyzing digital traces on the Internet. It allows the use of new data sources such as mobile phones, social media or satellite data for better understanding and in-depth study of social and demographic processes taking place in the modern world. At the same time, the increasing use of digital data is confronting the science of demography with a new data paradigm. The contradictory transition features of this new paradigm, called big data, have an impact on the formation of digital demography. This article examines Big Data paradigms and their role in the development of demography. The new opportunities provided by the innovative data sources created by the Big Data revolution to understand demographic processes and the technical and ethical challenges associated with the use of these sources are explored. Finally, the prospects for the development of digital demography in the Big Data environment are considered (pp.74-88).

Keywords: Big data, demography, digital data, social media, data revolution.
DOI : 10.25045/jpit.v12.i2.07
References
  • Weber I., State B. Digital Demography / Processings of the International World Wide Web Conference Committee (IW3C2), Perth, Australia. April 3–7, 2017, pp. 935–939.
  • Letouze E., Jutting J. Official statistics, big data and human development. Data-Pop Alliance White Paper Series, 2015, URL: https://www.paris21.org/
  • Aliguliyev R.M., Yusifov F.F. Milli E-demografiya sisteminin yaradilmasinin arkhitektur prinsiplari // Informasiya jamiyyati problemlari, 2021, №1, s. 3–17.
  • Alburez-Gutierrez D., Aref S., Gil-Clavel S. et al. Demography in the Digital Era: New Data Sources for Population Research. In: Arbia G., Peluso S., Pini A., Rivellini G. (eds.), Book of short Papers SIS2019. Pearson. 2019, URL: https://osf.io/preprints/socarxiv/
  • United Nations Global Pulse’s projects. URL: unglobalpulse.org/projects
  • Max Planck Institute for Demographic ResearchURL: https://www.demogr.mpg.de/en
  • Wittgenstein Centre for Demography and Global Human Capital.
    URL: http://www.wittgensteincentre.org/
  • Population Studies Center. URL: https://www.pop.upenn.edu/about)
  • The Fourth Paradigm. URL: https://en.wikipedia.org/wiki/The_Fourth_Paradigm
  • Billari F., Zagheni E. Big data and population processes: a revolution? In: Petrucci A., Verde R. (eds.) Statistics and Data Science: new challenges, new generations / Proceedings of the Conference of the Italian Statistical Society, Firenze University Press. Florence (Italy). 28–30 June, 2017, pp.167–178.
  • World Fertility Survey. URL: http://ghdx.healthdata.org/ /series/world-fertility-survey/
  • Moultrie T., Dorrington R., Hill A., Hil K., Timæus I., Zaba B. Tools for Demographic Estimation. IUSSP, Paris, 2013.
  • Courgeau D., Franck R. Demography, a fully formed science or a science in   the making? // Population-E, 2007, vol.62(01), pp.39–45.
  • Billari F.C. Integrating macro-and micro-level approaches in the explanation of population change // Population Studies, 2015, vol.69(1), pp. 11–20.
  • Zagheni E., Weber I. Demographic research with non-representative internet data // International Journal of Manpower, 2015, vol.36, no.1, pp. 13–25.
  • Integrated Public Use Microdata Series, IPUMS. URL: ipums.org.
  • Ruggles S. Big microdata for population research // Demography, 2014, vol. 51(1), pp. 287–297.
  • Thorvaldsen, G., Ostrem, N.O. Migration and the historical population register of Norway // Journal of Migration History, 2018, vol.4, no.2, pp. 237–248.
  • Lyngstad T.H., Skardhamar T. Nordic register data and their untapped potential for criminological knowledge // Crime and Justice, 2011, vol.40, no.1, pp. 613–645.
  • Robinson-Garcia N., Sugimoto C.R., Murray D. et al. The many faces of mobility: Using bibliometric data to measure the movement of scientists // Journal of Informetrics, 2019, vol.13, no.1, pp. 50–63.
  • Czaika M., Orazbayev S. The globalisation of scientific mobility // Applied Geography, 2018, vol.96, pp.1–10.
  • Fornes A, Llados J., Joan Mas J. et al. A bimodal ´ crowdsourcing platform for demographic historical manuscripts / Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage, ACM, 2014, pp. 103–108.
  • Kaplanis J., Gordon A., Shor T. et al. Quantitative analysis of population-scale family trees with millions of relatives // Science, 2018, vol. 360, no. 6385, pp. 171–175.
  • Cesare N., Lee H., McCormick T. et al. Promises and Pitfalls of Using Digital Traces for Demographic Research // Demography, 2018. vol. 55, no. 5, pp. 1979–1999.
  • Zagheni E., Garimella V.R.K., Weber I., State B. Inferring international and internal migration patterns from Twitter data / Proceedings of the 23rd International Conference on World Wide Web - WWW ’14 Companion, ACM Press, Seoul, Korea, 2014, pp. 439–444.
  • Fatehkia M., Kashyap R.,Weber I. Using Facebook ad data to track the global digital gender gap // World Development. 2018, vol. 107, pp. 189–209.
  • Potzschke S., Braun M. Migrant sampling using Facebook advertisements: A case study of Polish migrants in four European countries // Social Science Computer Review, 2017, vol. 35, no. 5, pp. 633–653.
  • Zagheni, E., Weber, I., Gummadi, K.: Leveraging Facebook’s advertising platform to monitor stocks of migrants // Population and Development Review, vol. 43(4), 721–734.
  • Yildiz D., Munson J., Vitali A. and et al. Using Twitter data for demographic research // Demographic Research, 2017, vol. 37, pp. 1477–1514.
  • Boas T.C., Christenson D.P., Glick D.M.: Recruiting large online samples in the United States and India: Facebook, Mechanical Turk, and Qualtrics // Political Science Research and Methods, 2018, pp. 1–19.
  • Fire M., Elovici Y. Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population // ACM Transactions on Intelligent Systems and Technology, 2015, vol. 6, issue 2, pp.1–24.
  • Lazer D. M., Kennedy R., King G., Vespignani A. The parable of Google Flu: Traps in big data analysis. Science, 2014, vol. 343, no. 6176, pp. 1203–1205.
  • Tamgno J.K., Faye R.M. & Lishou C. Verbal autopsies, mobile data collection for monitoring and warning causes of deaths / 15th International Conference on Advanced Communication Technology (ICACT), IEEE, 2013, pp. 495–501.
  • Eichstaedt J. C., Schwartz H. A., Kern M. L. et al. Psychological Language on Twitter Predicts County-Level Heart Disease Mortality // Psychological Science, 2015, vol. 26, no. 2, pp.159–169.
  • Tourassi G., Yoon, H. J. & Xu S. A Novel Web Informatics Approach for Automated Surveillance of Cancer Mortality Trends // Journal of Biomedical Informatics, 2016, vol. 61, pp.110–118.
  • Migration Data Portal: Big data, migration and human mobility. URL: https://migrationdataportal.org/themes/big-data-migration-and-human-mobility
  • Pepe E., Bajardi P., Gauvin L. et al. COVID-19 outbreak response: first assessment of mobility changes in Italy following lockdown, URL: 2020, https://covid19mm.github.io/in-progress/2020/03/13/first-report-assessment.html
  • Patel N.N., Stevens F. R., Huang Zh. et al. Improving Large Area Population Mapping Using Geotweet Densities // Transactions in GIS, 2017, 21, no. 2, pp. 317–331.
  • Gendronneau C., Wisniowski A., D.Yıldız et al. Measuring Labour Mobility and Migration Using Big Data: Exploring the Potential of Social-Media Data for Measuring EU Mobility Flows and Stocks of EU Movers, 2019, https://www.rand.org/pubs/external_publications/EP68038.html
  • Spyratos S., Vespe M., Natale F., Weber I., Zagheni E., Rango M. Quantifying international human mobility patterns using Facebook Network data // PLoS ONE, 2019, vol.14(10), pp. 1-22, https://doi.org/10.1371/journal. pone.0224134
  • Aliguliyev R.M., Hajirahimova M.Sh., Aliyeva A.S. Current scientific and theoretical problems of Big data // Problems of information society, 2016, №2, pp. 34–45.
  • Opal Project, opalproject.org/, accessed 08.04.2021.
  • Elaboration of a Recommendation on the ethics of artificial intelligence, URL: https://en.unesco.org/artificial-intelligence/ethics).
  • Artificial Intelligence, Big data and Fundamental Rights, https://fra.europa.eu/en/project/2018/artificial-intelligence-big-data-and-fundamental-rights
  • Data Protection, URL: https://www.iom.int/data-protection
  • Harvard Humanitarian Initiative, URL: https://hhi.harvard.edu/publications/signal-code- ethical-obligations-humanitarian-information-activities
  • Stefaan G. Verhulst, David Sangokoya, Data Collaboratives: Exchanging Data to Improve People’s Lives, 2015, URL: https://sverhulst.medium.com/data-collaboratives-exchanging-data-to-improve-people-s-lives-d0fcfc1bdd9a
  • Big Data for Migration Alliance:Harnessing new data sources and innovative methodologies for migeation, URL: https://data4migration.org)
  • Bohon A. Demography in the Big Data Revolution: Changing the Culture to Forge New Frontiers // Population Research and Policy Review, 2018, vol. 37, pp. 323–341.
  • King G. Preface: Big Data is not about the data! In Computational Social Science: Discovery and Prediction [R. Michael Alvarez, ed.], 2016, Cambridge University Press. URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:27719022
  • Tripathi R., Sharma P., Chakraborty P., Varadwaj P.K. Next-generation sequencing revolution through big data analytics // Frontiers in Life Science, 2016, vol.9, no.2, pp. 119–149.
  • Letouzé E. Demography, meet Big Data; Big Data, meet Demography: Reflections on the data-rich future of population science, 2015. URL: https://www.un.org/en/development/desa/population/events/pdf/
  • Bryant A. & Raja U. In the realm of Big Data // First Monday, 2014, vol. 19(2). URL: http://firstmonday.org/article/view/4991/3822. Accessed 3 Aprel 2021.
  • Maples J. N. Changes in US Ethnic Niches, 2005-2010, Doctoral Dissertation, 2012, University of Tennessee. URL: http://trace.tennessee.edu/socioetds/ 2012
  • Fiske S.T., Hauser R. M. Protecting human research participants in the age of big data // Proceedings of the National Academy of Sciences, 2014, vol.111, no. 38, pp.13675–13676.