Big data reveals socio-economic differences in phone use

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Image by ROBIN WORRALL on unsplash
Image by ROBIN WORRALL on unsplash

The apps on your phone may reveal your socio-economic status, according to European data-scientists. Researchers have used privacy-protected mobile-phone data from France to map differences in mobile phone usage across different neighbourhoods. According to the study, people living in high-income areas with more education tend to use their phones to access ‘traditional’ news media outlets, while people in low-income areas with less education use social media and video streaming platforms more frequently. Researchers suggest that their study could be useful in combating the spread of misinformation through social media in low-income communities, and believe that their methods could help keep population data more up-to-date as cities change.

Media release

From: The Royal Society

Internet inequalities - Do higher-educated neighbourhoods play more Clash of Clans or Candy Crush? Analysis of 3.7 billion mobile traffic records, collected by France’s leading mobile operator between 2015 and 2017, found significant geographic variation in the content consumed online – especially news, gaming and social media. This data gives an inexpensive and privacy-preserving way to understand the digital usage divide and, in turn, poverty, unemployment and economic growth, the authors said.

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Research The Royal Society, Web page
Journal/
conference:
Journal of the Royal Society Interface
Research:Paper
Organisation/s: Universidad Carlos III de Madrid, Spain
Funder: This work has been supported by the research project CANCAN (Content and Context based Adaptation in Mobile Networks), grant no. ANR-18-CE25-0011, funded by the French National Research Agency (ANR). The work of M.F. was partially supported by the Atracción de Talento Investigador grant no. 2019- T1/TIC-16037 NetSense, funded by Comunidad de Madrid. E.M. and I.U. acknowledge partial support by Ministerio de Economía, Industria y Competitividad, Gobierno de España, grant nos. FIS2016-78904-C3-3-P and PID2019-106811GB-C32.
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