On Inferring Monthly Expenses of Social Media Users: Towards Data and Approaches

  • Danila Vaganov
  • Alexander KalininEmail author
  • Klavdiya Bochenina
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)


Online social media is a sterling source for mining and examination of collective social attributes. This work investigates an inferring of the monthly expenses of social media users, which is relevant to the socio-economic status. The problem is treated as a classification task. We extract digital footprints of individuals from comprehensive real-world dataset collected from Russian social media, including friendship network, posts, subscriptions, and basic profile’s information. Users from social media were depersonalized and matched with bank profiles. Our first aim is evaluating the predictive ability of different explicit and latent representations of considered data. Our second aim is combining them in order to increase the quality of inference. For single features, results demonstrate a strong predictive ability of the network-based approaches. Regarding mixed approaches, combinations of network embeddings with demographic data and subscriptions vectors increase the correctness of classification.


Social media Graph embeddings Attribute inference User profiling 



This research is financially supported by The Russian Science Foundation, Agreement #17–71–30029 with co-financing of Bank Saint Petersburg. We are extremely grateful to Max Petrov for assistance with data collection from social media. We also very appreciate Amir Uteuov for his invaluable scientific help.


  1. 1.
    Aletras, N., Chamberlain, B.P.: Predicting Twitter user socioeconomic attributes with network and language information. In: Proceedings of the 29th on Hypertext and Social Media, pp. 20–24. ACM (2018)Google Scholar
  2. 2.
    Bernstein, B.: Language and social class. Br. J. Soc. 11(3), 271–276 (1960)CrossRefGoogle Scholar
  3. 3.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
  4. 4.
    Campbell, K.E., Marsden, P.V., Hurlbert, J.S.: Social resources and socioeconomic status. Soc. Netw. 8(1), 97–117 (1986)CrossRefGoogle Scholar
  5. 5.
    De Montjoye, Y.A., Hidalgo, C.A., Verleysen, M., Blondel, V.D.: Unique in the crowd: the privacy bounds of human mobility. Sci. Rep. 3, 1376 (2013)CrossRefGoogle Scholar
  6. 6.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9(Aug), 1871–1874 (2008)zbMATHGoogle Scholar
  7. 7.
    Fisher, J.E.: Social class and consumer behavior: the relevance of class and status. In: ACR North American Advances (1987)Google Scholar
  8. 8.
    Gao, J., Zhang, Y.C., Zhou, T.: Computational socioeconomics. Phys. Rep. 817, 1–104 (2019)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Garfinkel, S.L.: De-identification of personal information. Technical report, National Institute of Standards and Technology (2015)Google Scholar
  10. 10.
    Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)Google Scholar
  11. 11.
    Iqbal, S., Ismail, Z.: Buying behavior: gender and socioeconomic class differences on interpersonal influence susceptibility. Int. J. Bus. Soc. Sci. 2(4), 55–66 (2011)Google Scholar
  12. 12.
    Kreidl, M.: Perceptions of poverty and wealth in western and post-communist countries. Soc. Justice Res. 13(2), 151–176 (2000)CrossRefGoogle Scholar
  13. 13.
    Lampos, V., Aletras, N., Geyti, J.K., Zou, B., Cox, I.J.: Inferring the socioeconomic status of social media users based on behaviour and language. In: European Conference on Information Retrieval, pp. 689–695. Springer (2016)Google Scholar
  14. 14.
    Leo, Y., Karsai, M., Sarraute, C., Fleury, E.: Correlations and dynamics of consumption patterns in social-economic networks. Soc. Netw. Anal. Min. 8(1), 9 (2018)CrossRefGoogle Scholar
  15. 15.
    Abitbol, J.L., Karsai, M., Fleury, E.: Location, occupation, and semantics based socioeconomic status inference on Twitter. In: IEEE International Conference on Data Mining Workshops, ICDMW 2018, November 2018, pp. 1192–1199 (2019)Google Scholar
  16. 16.
    Luo, S., Morone, F., Sarraute, C., Travizano, M., Makse, H.A.: Inferring personal economic status from social network location. Nat. Commun. 8 (2017)Google Scholar
  17. 17.
    Macskassy, S.A., Provost, F.: Classification in networked data: a toolkit and a univariate case study. J. Mach. Learn. Res. 8(May), 935–983 (2007)Google Scholar
  18. 18.
    Matz, S.C., Menges, J.I., Stillwell, D.J., Schwartz, H.A.: Predicting individual-level income from Facebook profiles. PLoS ONE 14(3), 1–13 (2019)CrossRefGoogle Scholar
  19. 19.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Soc. 27(1), 415–444 (2001)CrossRefGoogle Scholar
  20. 20.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  21. 21.
    Page, S.: The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton University Press, Princeton (2007)Google Scholar
  22. 22.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 701–710. ACM, New York (2014).
  24. 24.
    Preoţiuc-Pietro, D., Lampos, V., Aletras, N.: An analysis of the user occupational class through Twitter content. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1754–1764 (2015).
  25. 25.
    Raedt, L.D., Kersting, K.: Statistical relational learning. In: Encyclopedia of Machine Learning, pp. 916–924 (2010)Google Scholar
  26. 26.
    Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning (2003)Google Scholar
  27. 27.
    Rizos, G., Papadopoulos, S., Kompatsiaris, Y.: Multilabel user classification using the community structure of online networks. PLoS ONE 12(3), e0173347 (2017)CrossRefGoogle Scholar
  28. 28.
    Schäfer, I., Hansen, H., Schön, G., Höfels, S., Altiner, A., Dahlhaus, A., Gensichen, J., Riedel-Heller, S., Weyerer, S., Blank, W.A., et al.: The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. First results from the multicare cohort study. BMC Health Serv. Res. 12(1), 89 (2012)CrossRefGoogle Scholar
  29. 29.
    Segalovich, I.: A fast morphological algorithm with unknown word guessing induced by a dictionary for a web search engine. In: Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA 2003. Citeseer (2003)Google Scholar
  30. 30.
    Tang, L., Liu, H.: Leveraging social media networks for classification. Data Min. Knowl. Discov. 23(3), 447–478 (2011)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Tsitsulin, A., Mottin, D., Karras, P., Müller, E.: VERSE: versatile graph embeddings from similarity measures. In: Proceedings of the 2018 World Wide Web Conference, WWW 2018, pp. 539–548. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2018).
  32. 32.
    Tucker-Drob, E.M., Briley, D.A.: Socioeconomic status modifies interest-knowledge associations among adolescents. Pers. Individ. Differ. 53(1), 9–15 (2012)CrossRefGoogle Scholar
  33. 33.
    Vaganov, D., Funkner, A., Kovalchuk, S., Guleva, V., Bochenina, K.: Forecasting purchase categories with transition graphs using financial and social data. In: International Conference on Social Informatics, pp. 439–454. Springer (2018)Google Scholar
  34. 34.
    Wu, L.Y., Fisch, A., Chopra, S., Adams, K., Bordes, A., Weston, J.: StarSpace: embed all the things! In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)Google Scholar
  35. 35.
    Zheleva, E., Getoor, L.: To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In: Proceedings of the 18th International Conference on World Wide Web, pp. 531–540. ACM (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Danila Vaganov
    • 1
  • Alexander Kalinin
    • 1
    Email author
  • Klavdiya Bochenina
    • 1
  1. 1.ITMO UniversitySaint-PetersburgRussia

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