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.
“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
The Results and Discussion > Topics section reports that income is associated with differences in topics discussed and word use among Twitter users; the Emotions section also reports differences in expressed sentiment. The paper does not establish that a platform’s overall demographic makeup causes these differences, nor does it examine which languages or viewpoints are observed. The passages provided have no page numbers. (LLM, conf=0.85)
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398 · see citing works
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337 · see citing works
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236 · see citing works
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,037 · see citing works
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Mann, H. B., & Whitney, D. R. (1947) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730491 · Cited by 14,320 · see citing works
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,649 · see citing works
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646 · see citing works
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206 · see citing works
Bayesian Learning for Neural Networks
Neal, R. M. (1996) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-0745-0 · Cited by 4,276 · see citing works
High income improves evaluation of life but not emotional well-being
Kahneman, D., & Deaton, A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1011492107 · Cited by 3,162 · see citing works
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Tumasjan, A., Sprenger, T. O., Sandner, P., & Welpe, I. M. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14009 · Cited by 2,647 · see citing works
Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007) · Perspectives on Psychological Science · doi:10.1111/j.1745-6916.2007.00047.x · Cited by 2,361 · see citing works
THE BIG FIVE PERSONALITY TRAITS, GENERAL MENTAL ABILITY, AND CAREER SUCCESS ACROSS THE LIFE SPAN
Judge, T. A., Higgins, C. A., Thoresen, C. J., & Barrick, M. R. (1999) · Personnel Psychology · doi:10.1111/j.1744-6570.1999.tb00174.x · Cited by 2,184 · see citing works
Vapnik, V. N. 1. (1998) · Cited by 2,163 · see citing works
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700 · see citing works
Will Money Increase Subjective Well-Being?
Diener, E., & Biswas-Diener, R. (2002) · Social Indicators Research · doi:10.1023/a:1014411319119 · Cited by 1,555 · cited 1× · see citing works
“The relationship between life satisfaction and income is widely debated [54].”
BackgroundSparse Gaussian Processes using Pseudo-inputs
Snelson, E., & Ghahramani, Z. (2005) · Cited by 1,415 · see citing works
Allerton, D. J. (1995) · Journal of Atmospheric and Terrestrial Physics · doi:10.1016/0021-9169(95)90017-9 · Cited by 1,103 · see citing works
Computer-based personality judgments are more accurate than those made by humans
Youyou, W., Kosinski, M., & Stillwell, D. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418680112 · Cited by 1,086 · see citing works
Cheng, Z., Caverlee, J., & Lee, K. (2010) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1871437.1871535 · Cited by 1,085 · see citing works
Statistical Models: Theory and Practice
Freedman, D. A. (2006) · Technometrics · doi:10.1198/tech.2006.s403 · Cited by 1,001 · see citing works
Freedman, D. A. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511815867 · Cited by 947 · see citing works
Quantifying Mental Health Signals in Twitter
Coppersmith, G., Dredze, M., & Harman, C. (2014) · Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w14-3207 · Cited by 722 · see citing works
Classifying latent user attributes in twitter
Rao, D., Yarowsky, D., Shreevats, A., & Gupta, M. (2010) · Proceedings of the 2nd international workshop on Search and mining user-generated contents · doi:10.1145/1871985.1871993 · Cited by 655 · see citing works
A Latent Variable Model for Geographic Lexical Variation
Eisenstein, J., O'Connor, B., Smith, N. A., & Xing, E. P. (2018) · Figshare · doi:10.1184/r1/6475412 · Cited by 584 · see citing works
Discriminating Gender on Twitter
Burger, J. D., Henderson, J. C., Kim, G., & Zarrella, G. (2011) · Cited by 581 · see citing works
A Machine Learning Approach to Twitter User Classification
Marco Pennacchiotti, B., & Popescu, A.-M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14139 · Cited by 548 · see citing works
Tweets from Justin Bieber's heart
Hecht, B. J., Hong, L., Suh, B., & Ed, H. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1978976 · Cited by 466 · see citing works
The Gender Earnings Gap: Learning from International Comparisons
Blau, F. D., & Kahn, L. M. (2016) · American Economic Review · Cited by 454 · see citing works
Bernstein, B. B. (1960) · British Journal of Sociology · doi:10.2307/586750 · Cited by 439 · see citing works
Sloan, L., Morgan, J., Burnap, P., & Williams, M. L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0115545 · Cited by 368 · see citing works
Zamal, F. A., Liu, W. J., & Ruths, D. A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14340 · Cited by 310 · see citing works
Diffusion of Lexical Change in Social Media
Eisenstein, J., O'Connor, B., Smith, N. A., & Xing, E. P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0113114 · Cited by 245 · see citing works
Developing Age and Gender Predictive Lexica over Social Media
Sap, M., Park, G., Eichstaedt, J. C., Kern, M. L., Stillwell, D. J., Kosiński, M., Ungar, L., & Schwartz, H. A. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1121 · Cited by 235 · see citing works
An analysis of the user occupational class through Twitter content
Preoțiuc-Pietro, D., Lampos, V., & Αλέτρας, Ν. (2015) · doi:10.3115/v1/p15-1169 · Cited by 222 · see citing works
Self-citationNowcasting Events from the Social Web with Statistical Learning
Lampos, V., & Cristianini, N. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2337542.2337557 · Cited by 213 · see citing works
Self-citationClassifying Political Orientation on Twitter: It’s Not Easy!
Cohen, R., & Ruths, D. A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14434 · Cited by 208 · see citing works
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media
Volkova, S., Wilson, T., & Yarowsky, D. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1187 · Cited by 191 · see citing works
Self-citationAuthor Age Prediction from Text using Linear Regression
Nguyen, D., A. Smith, N., & Rosé, C. P. (2018) · Figshare · doi:10.1184/r1/6473069 · Cited by 178 · see citing works
Inferring Latent User Properties from Texts Published in Social Media
Volkova, S., Bachrach, Y., Armstrong, M., & Sharma, V. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9271 · Cited by 151 · see citing works
Self-citationRout, D., Bontcheva, K., Preoțiuc-Pietro, D., & Cohn, T. (2013) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2481492.2481494 · Cited by 119 · see citing works
Self-citationInferring User Political Preferences from Streaming Communications
Volkova, S., Coppersmith, G., & Durme, B. V. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1018 · Cited by 119 · see citing works
Self-citationWhy Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment
Nguyen, D., Trieschnigg, D., Doğruöz, A. S., Gravel, R., Theune, M., Meder, T., & de Jong, F. M. G. (2014) · Data Archiving and Networked Services (DANS) · Cited by 104 · see citing works
Confounds and consequences in geotagged twitter data
Pavalanathan, U., & Eisenstein, J. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1256 · Cited by 104 · see citing works
Affluence, Feelings of Stress, and Well-being
Ng, S. H., Diener, E., Aurora, R., & Harter, J. K. (2008) · Social Indicators Research · doi:10.1007/s11205-008-9422-5 · Cited by 91 · see citing works
Trendminer: An Architecture for Real Time Analysis of Social Media Text
Preoțiuc-Pietro, D., Samangooei, S., Cohn, T., Gibbins, N. M., & Niranjan, M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i3.14348 · Cited by 75 · see citing works
Self-citationPredicting and Characterising User Impact on Twitter
Lampos, V., Αλέτρας, Ν., Preoțiuc-Pietro, D., & Cohn, T. (2014) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1043 · Cited by 74 · see citing works
Self-citationSOC2010: revision of the Standard Occupational Classification
Elias, P., & Birch, M. (2010) · Economic & Labour Market Review · doi:10.1057/elmr.2010.96 · Cited by 35 · see citing works
I’m a Belieber: Social Roles via Self-identification and Conceptual Attributes
Beller, C. E., Knowles, R., Harman, C., Bergsma, S., Mitchell, M., & Durme, B. V. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2030 · Cited by 29 · see citing works
Flagged first, then by citations
1 of the 59 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [55]Bernstein B. Class, Codes and Control: Applied Studies Towards a Sociology of Language. vol. 2 Psychology Press; 2003.
Cited by
Sorted by citations
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 · see citing works
Αλέτρας, Ν., Tsarapatsanis, D., Preoțiuc-Pietro, D., & Lampos, V. (2016) · PeerJ Computer Science · doi:10.7717/peerj-cs.93 · Cited by 813 · see citing works
Self-citationSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
Social media data for conservation science: A methodological overview
Toivonen, T., Heikinheimo, V., Fink, C., Hausmann, A., Hiippala, T., Järv, O., Tenkanen, H., & Minin, E. D. (2019) · Biological Conservation · doi:10.1016/j.biocon.2019.01.023 · Cited by 468 · see citing works
The social impact of natural language processing
Hovy, D., & Spruit, S. L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p16-2096 · Cited by 329 · see citing works
Beyond Binary Labels: Political Ideology Prediction of Twitter Users
Preoțiuc-Pietro, D., Liu, Y., Hopkins, D. J., & Ungar, L. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1068 · Cited by 291 · see citing works
Self-citationA survey of Twitter research: Data model, graph structure, sentiment analysis and attacks
Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114006 · Cited by 279 · see citing works
Computational Sociolinguistics: A Survey
Nguyen, D., Doğruöz, A. S., Rosé, C. P., & de Jong, F. M. G. (2016) · Computational Linguistics · doi:10.1162/coli_a_00258 · Cited by 231 · see citing works
Analyzing Personality through Social Media Profile Picture Choice
Leqi, L., Preoțiuc-Pietro, D., Samani, Z. R., Moghaddam, M. E., & Ungar, L. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14738 · Cited by 219 · see citing works
Self-citationGeneralisation in named entity recognition: A quantitative analysis
Augenstein, I., Derczynski, L., & Bontcheva, K. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.01.012 · Cited by 123 · see citing works
Gao, J. G., Zhang, Y., & Zhou, T. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.05.002 · Cited by 118 · see citing works
Lyu, H., Wang, J., Wu, W., Duong, V., Zhang, X., Dye, T., & Luo, J. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.08.001 · Cited by 101 · see citing works
What demographic attributes do our digital footprints reveal? A systematic review
Hinds, J. M., & Joinson, A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207112 · Cited by 98 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Predicting age groups of Twitter users based on language and metadata features
Morgan‐López, A. A., Kim, A. E., Chew, R., & Ruddle, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183537 · Cited by 84 · see citing works
Studying Personality through the Content of Posted and Liked Images on Twitter
Guntuku, S. C., Lin, W., Carpenter, J. M., Ng, W. K., Ungar, L., & Preoțiuc-Pietro, D. (2017) · ACM Web Science Conference (WebSci) · doi:10.1145/3091478.3091522 · Cited by 79 · see citing works
Self-citationCategorizing and Inferring the Relationship between the Text and Image of Twitter Posts
Vempala, A., & Preoțiuc-Pietro, D. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1272 · Cited by 78 · see citing works
Self-citationIncorporating Dialectal Variability for Socially Equitable Language Identification
Jurgens, D., Tsvetkov, Y., & Jurafsky, D. (2017) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p17-2009 · Cited by 108 · see citing works
Sloan, L. (2017) · Social Media + Society · doi:10.1177/2056305117698981 · Cited by 75 · see citing works
Aragón, M. E., López-Monroy, A. P., González, L. C., & Montes-y-Gómez, M. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3075638 · Cited by 73 · see citing works
Godinho, C. P., Prata, C. S., Pinto, S. N., Cardoso, C. L., Bandarra, N. M., Fernandes, F., & Sá‐Correia, I. (2018) · Scientific Reports · doi:10.1038/s41598-018-26128-7 · Cited by 72 · see citing works
Volkova, S., & Bachrach, Y. (2015) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2014.0609 · Cited by 71 · see citing works
Self-citationAutomatically Identifying Complaints in Social Media
Preoțiuc-Pietro, D., Găman, M., & Αλέτρας, Ν. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1495 · Cited by 68 · see citing works
Self-citationStudying the Dark Triad of Personality through Twitter Behavior
Preoțiuc-Pietro, D., Carpenter, J. M., Giorgi, S., & Ungar, L. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983822 · Cited by 68 · see citing works
Self-citationExploring Stylistic Variation with Age and Income on Twitter
Flek, L., Preoțiuc-Pietro, D., & Ungar, L. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2051 · Cited by 66 · see citing works
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Volkova, S., & Bachrach, Y. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1148 · Cited by 65 · see citing works
Self-citationLock, O., & Pettit, C. J. (2020) · Geo-spatial Information Science · doi:10.1080/10095020.2020.1815596 · Cited by 63 · see citing works
Inferring the Socioeconomic Status of Social Media Users Based on Behaviour and Language
Lampos, V., Αλέτρας, Ν., Geyti, J. K., Zou, B., & Cox, I. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30671-1_54 · Cited by 61 · see citing works
Self-citationDevkota, B., Miyazaki, H., Witayangkurn, A., & Kim, S. M. (2019) · Sustainability · doi:10.3390/su11174718 · Cited by 58 · see citing works
Baghal, T. A., Sloan, L., Jessop, C., Williams, M. L., & Burnap, P. (2019) · Social Science Computer Review · doi:10.1177/0894439319828011 · Cited by 57 · see citing works
Enhancing Feature Selection Using Word Embeddings
Lampos, V., Zou, B., & Cox, I. J. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052622 · Cited by 53 · see citing works
Self-citationWang, Y., Li, Y., & Luo, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14783 · Cited by 52 · see citing works
Analyzing Biases in Human Perception of User Age and Gender from Text
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Zou, B., Lampos, V., Gorton, R., & Cox, I. J. (2016) · International Conference on Digital Health · doi:10.1145/2896338.2896372 · Cited by 54 · see citing works
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Using Social Media to Predict the Future: A Systematic Literature Review
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Self-citationSocial media analysis during political turbulence
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Mueller, A., Wood-Doughty, Z., Amir, S., Dredze, M. H., & Nobles, A. L. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449181 · Cited by 50 · see citing works
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Self-citationAlshaabi, T., Adams, J. L., Arnold, M., Minot, J. R., Dewhurst, D. R., Reagan, A. J., Danforth, C. M., & Dodds, P. S. (2021) · Science Advances · doi:10.1126/sciadv.abe6534 · Cited by 51 · see citing works
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Predicting individual-level income from Facebook profiles
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Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
Zubiaga, A., Alex Voss, L., Procter, R., Liakata, M., Wang, B., & Tsakalidis, A. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2698463 · Cited by 40 · see citing works
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Kim, A. E., Miano, T., Chew, R., Eggers, M. E., & Nonnemaker, J. M. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.8060 · Cited by 37 · see citing works
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Fung, I. C., Yin, J., Pressley, K. D., Duke, C. H., Mo, C., Liang, H., Fu, K., Tse, Z. T. H., & Hou, S. (2019) · Data · doi:10.3390/data4020084 · Cited by 16 · see citing works
Xie, Y., & Ying, Q. (2025) · International Review of Financial Analysis · doi:10.1016/j.irfa.2025.104325 · Cited by 15 · see citing works
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Hovy, D. (2018) · doi:10.18653/v1/w18-1106 · Cited by 15 · see citing works
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Sun, Q., Huang, X., & Liu, Z. (2021) · The Computer Journal · doi:10.1093/comjnl/bxab210 · Cited by 15 · see citing works
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Reuel, A., Peralta, S., Sedoc, J., Sherman, G. T., & Ungar, L. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.83 · Cited by 14 · see citing works
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Snefjella, B., Schmidtke, D., & Kuperman, V. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206188 · Cited by 14 · see citing works
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Mu, Y., & Αλέτρας, Ν. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.325 · Cited by 14 · see citing works
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Vennila, V., & Kannan, A. R. (2019) · International Journal of Fuzzy Systems · doi:10.1007/s40815-018-0597-x · Cited by 14 · see citing works
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Smirnov, I. (2020) · EPJ Data Science · doi:10.1140/epjds/s13688-020-00245-8 · Cited by 15 · see citing works
Lokala, U., Phukan, O. C., Dastidar, T. G., Lamy, F. R., Daniulaityte, R., & Sheth, A. (2024) · JMIRx Med · doi:10.2196/48519 · Cited by 13 · see citing works
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Tsakalidis, A., Liakata, M., Damoulas, T., Jellinek, B., Guo, W., & Cristea, A. I. (2016) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33727657 · Cited by 14 · see citing works
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Preoțiuc-Pietro, D., Carpenter, J. M., & Ungar, L. (2017) · Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) · doi:10.18653/v1/w17-2903 · Cited by 13 · see citing works
Self-citation
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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