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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)

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.

Keywords

Social media Graph embeddings Attribute inference User profiling 

Notes

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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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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