Abstract
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 VK.com, 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.
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Notes
- 1.
We use the comprehensive collection of stop-words for the Russian language, which is available at “https://github.com/stopwords-iso/stopwords-ru.”
References
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)
Bernstein, B.: Language and social class. Br. J. Soc. 11(3), 271–276 (1960)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
Campbell, K.E., Marsden, P.V., Hurlbert, J.S.: Social resources and socioeconomic status. Soc. Netw. 8(1), 97–117 (1986)
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)
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)
Fisher, J.E.: Social class and consumer behavior: the relevance of class and status. In: ACR North American Advances (1987)
Gao, J., Zhang, Y.C., Zhou, T.: Computational socioeconomics. Phys. Rep. 817, 1–104 (2019)
Garfinkel, S.L.: De-identification of personal information. Technical report, National Institute of Standards and Technology (2015)
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)
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)
Kreidl, M.: Perceptions of poverty and wealth in western and post-communist countries. Soc. Justice Res. 13(2), 151–176 (2000)
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)
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)
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)
Luo, S., Morone, F., Sarraute, C., Travizano, M., Makse, H.A.: Inferring personal economic status from social network location. Nat. Commun. 8 (2017)
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)
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)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Soc. 27(1), 415–444 (2001)
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)
Page, S.: The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton University Press, Princeton (2007)
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)
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). https://doi.org/10.1145/2623330.2623732
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). http://aclweb.org/anthology/P15-1169
Raedt, L.D., Kersting, K.: Statistical relational learning. In: Encyclopedia of Machine Learning, pp. 916–924 (2010)
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)
Rizos, G., Papadopoulos, S., Kompatsiaris, Y.: Multilabel user classification using the community structure of online networks. PLoS ONE 12(3), e0173347 (2017)
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)
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)
Tang, L., Liu, H.: Leveraging social media networks for classification. Data Min. Knowl. Discov. 23(3), 447–478 (2011)
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). https://doi.org/10.1145/3178876.3186120
Tucker-Drob, E.M., Briley, D.A.: Socioeconomic status modifies interest-knowledge associations among adolescents. Pers. Individ. Differ. 53(1), 9–15 (2012)
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)
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)
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)
Acknowledgment
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.
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Vaganov, D., Kalinin, A., Bochenina, K. (2020). On Inferring Monthly Expenses of Social Media Users: Towards Data and Approaches. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_71
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