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Socioeconomic Patterns of Twitter User Activity
Name
entropy-23-00780-v3.pdf
Size
2.08 MB
Format
Adobe PDF
Checksum (MD5)
04ca1d6ef6f34ca2bf32f1ccaae5ccb1
Author(s)
Abitbol, Jacob Levy
Morales, Alfredo J.
Date Issued
June 19, 2021
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Entropy 23 (6): 780 (2021)
Version
Final published version
Abstract
Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual’s income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups.
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.3390/e23060780
