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.”
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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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