Duration: September 1, 2018 – August 31, 2021 (35 months)
Large amounts of data are available today to study human behavior: from social media, mobile phone calls, digital texts, sensory information, personal devices, the use of search engines—and more. The industry that collects, combines, sells, and analyzes digital footprints is developing at lightning speed. These data offer greater possibilities for social sciences, and the project “Uncovering patterns of inequalities and imbalances in large-scale networks” aims to explore them.
Once analyzed through sociological methods only, the project’s aim focus on spatial dimensions of inequalities that can be observed from large-scale social, communication, and business networks—shedding light on features and imbalances that have been hidden by limitations of traditional methods. The uniqueness of the research lies in the close cooperation of social and natural scientists, by taking advantage of comprehensive datasets now accessible.
Some of these datasets come from a telecom and a popular social network in Hungary. The researcher will complement the data with open-source income data. This will allow him to calculate income inequalities and interregional connections at the individual level. By doing so, it will be possible to compare community differences within particular Hungarian towns. Other data sources will be considered: some aim to analyze inequalities in house rents; others, to help identify corruption risks and relationships between social capital, network density, and regional economic development.
The research is timely because identifying regional inequalities is key to informing policies on migration, for example. Disparities in house rents also have a societal impact: they determine school choice and may cause segregation. Throwing light on corruption risks may help tackle the issue that undermines inequality policies. Analyzing how people are connected can uncover what social groups have access to social activities, and why some groups don’t. The project’s outputs include the publication of results in ranked journals and conference presentations. Grounded on solid scientific ethical principles and guidelines, the data used will be protected and the project will release no personal or sensitive information.
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