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Inferring User Social Class in Online Social Networks

Published: 24 August 2014 Publication History

Abstract

Although information posted in online social networks has proven to be accurate enough to monitor and predict real world phenomena, not a lot is known about users spreading this information. Previous studies have explored user public data to infer personal attributes such as gender, age and location, but one aspect is yet to be explored: social class. Assuming an objective definition of social class, based on income and wealth, we propose a new method to automatically generate a user social class dataset, taking advantage of Foursquare user interactions and Twitter messages. The basic idea to build our social class dataset is: the wealthier the place, the richer the users who usually visit it. We build our dataset by describing users using the contents of their tweets, and a machine learning algorithm is employed to automatically generate social class classification models. Our experimental results show that, considering social class divisions into two, three and four segments, the predictive accuracies of our models varied from 57% to 73%.

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Cited By

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  • (2021)Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategyPLOS ONE10.1371/journal.pone.025463816:7(e0254638)Online publication date: 13-Jul-2021
  • (2020)Big Trajectory Data Mining: A Survey of Methods, Applications, and ServicesSensors10.3390/s2016457120:16(4571)Online publication date: 14-Aug-2020
  • (2019)Predicting Socio-Economic Levels of Individuals via App Usage RecordsMachine Learning and Intelligent Communications10.1007/978-3-030-32388-2_17(199-210)Online publication date: 28-Oct-2019
  • Show More Cited By

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cover image ACM Conferences
SNAKDD'14: Proceedings of the 8th Workshop on Social Network Mining and Analysis
August 2014
90 pages
ISBN:9781450331920
DOI:10.1145/2659480
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 August 2014

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Cited By

View all
  • (2021)Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategyPLOS ONE10.1371/journal.pone.025463816:7(e0254638)Online publication date: 13-Jul-2021
  • (2020)Big Trajectory Data Mining: A Survey of Methods, Applications, and ServicesSensors10.3390/s2016457120:16(4571)Online publication date: 14-Aug-2020
  • (2019)Predicting Socio-Economic Levels of Individuals via App Usage RecordsMachine Learning and Intelligent Communications10.1007/978-3-030-32388-2_17(199-210)Online publication date: 28-Oct-2019
  • (2015)Twitter Population Sample Bias and its impact on predictive outcomesProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201510.1145/2808797.2809328(1254-1261)Online publication date: 25-Aug-2015

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