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Studying User Income through Language, Behaviour and Affect in Social Media

Preotiuc-Pietro, D; Volkova, S; Lampos, V; Bachrach, Y; Aletras, N; (2015) Studying User Income through Language, Behaviour and Affect in Social Media. PLoS ONE , 10 (9) , Article e0138717. 10.1371/journal.pone.0138717. Green open access

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Abstract

Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions.

Type: Article
Title: Studying User Income through Language, Behaviour and Affect in Social Media
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0138717
Publisher version: http://dx.doi.org/10.1371/journal.pone.0138717
Language: English
Additional information: © 2015 Preoţiuc-Pietro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1471375
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