The DOI matches its Crossref record; 58 of the 59 references it cites were found in a registry or an index.
Studying User Income through Language, Behaviour and Affect in Social Media
Open accessAbstract
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.
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“The demographic makeup of a platform's users has an effect on the languages, topics, and viewpoints that are observed”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Preoţiuc-Pietro et al., 2015
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Li, S., Wang, Y., Xue, J., Zhao, N., & Zhu, T. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17062032 · Cited by 1,835
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Metadata
- Authors
- Preoţiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., & Aletras, N.
- Affiliations
- Johns Hopkins University (US)
- University College London (GB)
- Microsoft Research (United Kingdom) (GB)
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-09-22
- Volume
- 10
- Issue
- 9
- Pages
- e0138717
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4578862
- OpenAlex
- W1948823840
- Cited by
- 219
- References
- 59
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 22 · 2023: 16 · 2022: 13 · 2021: 19 · 2020: 23 · 2019: 29 · 2018: 25 · 2017: 30 · 2016: 31 · 2015: 2
- FWCI
- 28.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Templeton Religion Trust (TRT0048)
- Engineering and Physical Sciences Research Council (EP/K031953/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- income prediction, social media, Twitter, user demographics, Gaussian processes, sentiment analysis, emotions, language use, topic, gender differences, age differences, fear and anger
- MeSH terms
- Affect, Data Collection, Educational Status, Female, Humans, Income, Intelligence, Language, Male, Reproducibility of Results, Linear Models, Social Media
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