Optimal Proxy Selection for Socioeconomic Status Inference on Twitter
Individual socioeconomic status inference from online traces is a remarkably difficult task. While current methods commonly train predictive models on incomplete data by appending socioeconomic information of residential areas or professional occupation profiles, little attention has been paid to ho...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/6059673 |
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| author | Jacob Levy Abitbol Eric Fleury Márton Karsai |
| author_facet | Jacob Levy Abitbol Eric Fleury Márton Karsai |
| author_sort | Jacob Levy Abitbol |
| collection | DOAJ |
| description | Individual socioeconomic status inference from online traces is a remarkably difficult task. While current methods commonly train predictive models on incomplete data by appending socioeconomic information of residential areas or professional occupation profiles, little attention has been paid to how well this information serves as a proxy for the individual demographic trait of interest when fed to a learning model. Here we address this question by proposing three different data collection and combination methods to first estimate and, in turn, infer the socioeconomic status of French Twitter users from their online semantics. We assess the validity of each proxy measure by analyzing the performance of our prediction pipeline when trained on these datasets. Despite having to rely on different user sets, we find that training our model on professional occupation provides better predictive performance than open census data or remote sensed expert annotation of habitual environments. Furthermore, we release the tools we developed in the hope it will provide a generalizable framework to estimate socioeconomic status of large numbers of Twitter users as well as contribute to the scientific discussion on social stratification and inequalities. |
| format | Article |
| id | doaj-art-41b0fe02eea24d7ca9f267e1e12d1199 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-41b0fe02eea24d7ca9f267e1e12d11992025-08-20T03:55:40ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/60596736059673Optimal Proxy Selection for Socioeconomic Status Inference on TwitterJacob Levy Abitbol0Eric Fleury1Márton Karsai2Univ Lyon, Inria, CNRS, ENS de Lyon, Université Claude Bernard Lyon 1, LIP UMR 5668, F-69007 Lyon, FranceInria, F-75012 Paris, FranceUniv Lyon, Inria, CNRS, ENS de Lyon, Université Claude Bernard Lyon 1, LIP UMR 5668, F-69007 Lyon, FranceIndividual socioeconomic status inference from online traces is a remarkably difficult task. While current methods commonly train predictive models on incomplete data by appending socioeconomic information of residential areas or professional occupation profiles, little attention has been paid to how well this information serves as a proxy for the individual demographic trait of interest when fed to a learning model. Here we address this question by proposing three different data collection and combination methods to first estimate and, in turn, infer the socioeconomic status of French Twitter users from their online semantics. We assess the validity of each proxy measure by analyzing the performance of our prediction pipeline when trained on these datasets. Despite having to rely on different user sets, we find that training our model on professional occupation provides better predictive performance than open census data or remote sensed expert annotation of habitual environments. Furthermore, we release the tools we developed in the hope it will provide a generalizable framework to estimate socioeconomic status of large numbers of Twitter users as well as contribute to the scientific discussion on social stratification and inequalities.http://dx.doi.org/10.1155/2019/6059673 |
| spellingShingle | Jacob Levy Abitbol Eric Fleury Márton Karsai Optimal Proxy Selection for Socioeconomic Status Inference on Twitter Complexity |
| title | Optimal Proxy Selection for Socioeconomic Status Inference on Twitter |
| title_full | Optimal Proxy Selection for Socioeconomic Status Inference on Twitter |
| title_fullStr | Optimal Proxy Selection for Socioeconomic Status Inference on Twitter |
| title_full_unstemmed | Optimal Proxy Selection for Socioeconomic Status Inference on Twitter |
| title_short | Optimal Proxy Selection for Socioeconomic Status Inference on Twitter |
| title_sort | optimal proxy selection for socioeconomic status inference on twitter |
| url | http://dx.doi.org/10.1155/2019/6059673 |
| work_keys_str_mv | AT jacoblevyabitbol optimalproxyselectionforsocioeconomicstatusinferenceontwitter AT ericfleury optimalproxyselectionforsocioeconomicstatusinferenceontwitter AT martonkarsai optimalproxyselectionforsocioeconomicstatusinferenceontwitter |