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Analyzing gender inequality through large-scale Facebook advertising data

Author

Listed:
  • David Garcia

    (Complexity Science Hub Vienna, 1080 Vienna, Austria; Section for Science of Complex Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria)

  • Yonas Mitike Kassa

    (IMDEA Networks Institute, 28918 Leganés, Spain; Department of Telematic Engineering, Universidad Carlos III de Madrid, 28911 Leganés, Spain)

  • Angel Cuevas

    (Department of Telematic Engineering, Universidad Carlos III de Madrid, 28911 Leganés, Spain)

  • Manuel Cebrian

    (Data61, Commonwealth Scientific and Industrial Research Organisation, 3008 Melbourne, Australia; The Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139)

  • Esteban Moro

    (The Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139; Grupo Interdisciplinar de Sistemas Complejos, Department of Mathematics, Universidad Carlos III de Madrid, 28911 Leganes, Spain)

  • Iyad Rahwan

    (The Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139; Institute for Data, Systems & Society, Massachusetts Institute of Technology, Cambridge, MA 02139)

  • Ruben Cuevas

    (Department of Telematic Engineering, Universidad Carlos III de Madrid, 28911 Leganés, Spain)

Abstract

Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media, in particular, are prone to gender inequality, an important issue given the link between social media use and employment. Understanding gender inequality in social media is a challenging task due to the necessity of data sources that can provide large-scale measurements across multiple countries. Here, we show how the Facebook Gender Divide (FGD), a metric based on aggregated statistics of more than 1.4 billion users in 217 countries, explains various aspects of worldwide gender inequality. Our analysis shows that the FGD encodes gender equality indices in education, health, and economic opportunity. We find gender differences in network externalities that suggest that using social media has an added value for women. Furthermore, we find that low values of the FGD are associated with increases in economic gender equality. Our results suggest that online social networks, while suffering evident gender imbalance, may lower the barriers that women have to access to informational resources and help to narrow the economic gender gap.

Suggested Citation

  • David Garcia & Yonas Mitike Kassa & Angel Cuevas & Manuel Cebrian & Esteban Moro & Iyad Rahwan & Ruben Cuevas, 2018. "Analyzing gender inequality through large-scale Facebook advertising data," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(27), pages 6958-6963, July.
    • Handle: RePEc:nas:journl:v:115:y:2018:p:6958-6963
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    Citations

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    Cited by:

    1. Liberini, Federica & Redoano, Michela & Russo, Antonio & Cuevas, Angel & Cuevas, Ruben, 2018. "Politics in the Facebook Era Evidence from the 2016 US Presidential Elections," The Warwick Economics Research Paper Series (TWERPS) 1181, University of Warwick, Department of Economics.
    2. Laetitia Gauvin & Michele Tizzoni & Simone Piaggesi & Andrew Young & Natalia Adler & Stefaan Verhulst & Leo Ferres & Ciro Cattuto, 2020. "Gender gaps in urban mobility," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.
    3. Alexander, Monica & Zagheni, Emilio & Polimis, Kivan, 2019. "The impact of Hurricane Maria on out-migration from Puerto Rico: Evidence from Facebook data," SocArXiv 39s6c, Center for Open Science.
    4. Ugofilippo Basellini & Diego Alburez-Gutierrez & Emanuele Del Fava & Daniela Perrotta & Marco Bonetti & Carlo Giovanni Camarda & Emilio Zagheni, 2020. "Linking excess mortality to Google mobility data during the COVID-19 pandemic in England and Wales," Working Papers axniwfk3qpl52ayy4p-i, French Institute for Demographic Studies.
    5. Monica Alexander & Kivan Polimis & Emilio Zagheni, 2022. "Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(1), pages 1-28, February.
    6. Ugofilippo Basellini & Diego Alburez-Gutierrez & Emanuele Del Fava & Daniela Perrotta & Marco Bonetti & Carlo Giovanni Camarda & Emilio Zagheni, 2020. "Linking excess mortality to Google mobility data during the COVID-19 pandemic in England and Wales," Working Papers axehlaypkgkzhr-blqv4, French Institute for Demographic Studies.
    7. Nadine Bachmann & Shailesh Tripathi & Manuel Brunner & Herbert Jodlbauer, 2022. "The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    8. Ridhi Kashyap & Masoomali Fatehkia & Reham Al Tamime & Ingmar Weber, 2020. "Monitoring global digital gender inequality using the online populations of Facebook and Google," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(27), pages 779-816.

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