Personal Credit Profiling via Latent User Behavior Dimensions on Social Media

  • Guangming Guo
  • Feida Zhu
  • Enhong ChenEmail author
  • Le Wu
  • Qi Liu
  • Yingling Liu
  • Minghui Qiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9652)


Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, existing credit scoring methods, mainly relying on financial data, face severe challenges when tackling the heterogeneous social data. Moreover, social data only contains extremely weak signals about users’ credit label. To that end, we put forward a Latent User Behavior Dimension based Credit Model (LUBD-CM) to capture these small signals for personal credit profiling. LUBD-CM learns users’ hidden behavior habits and topic distributions simultaneously, and represents each user at a much finer granularity. Specifically, we take a real-world Sina Weibo dataset as the testbed for personal credit profiling evaluation. Experiments conducted on the dataset demonstrate the effectiveness of our approach: (1) User credit label can be predicted using LUBD-CM with a considerable performance improvement over state-of-the-art baselines; (2) The latent behavior dimensions have very good interpretability in personal credit profiling.


Credit Risk User Attribute Behavior Data Social Data User Generate Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the National High Technology Research and Development Program of China (Grant No. 2014AA015203), the Science and Technology Program for Public Wellbeing (Grant No. 2013GS340302) and the CCF-Tencent Open Research Fund. This work was also partially supported by the Pinnacle Lab for Analytics @ Singapore Management University.


  1. 1.
    Arminger, G., Enache, D., Bonne, T.: Analyzing credit risk data: a comparison of logistic discrimination, classification tree analysis, and feedforward networks. Comput. Stat. 12(2), 293–310 (1997)zbMATHGoogle Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  3. 3.
    Burger, J.D., Henderson, J.C., Kim, G., Zarrella, G.: Discriminating gender on twitter. In: EMNLP, pp. 1301–1309 (2011)Google Scholar
  4. 4.
    Crook, J.N., Edelman, D.B., Thomas, L.C.: Recent developments in consumer credit risk assessment. Eur. J. Oper. Res. 183(3), 1447–1465 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Dong, Y., Yang, Y., Tang, J., Yang, Y., Chawla, N.V.: Inferring user demographics and social strategies in mobile social networks. In: KDD, pp. 15–24 (2014)Google Scholar
  6. 6.
    Eisenbeis, R.A.: Problems in applying discriminant analysis in credit scoring models. J. Bank. Finance 2(3), 205–219 (1978)CrossRefGoogle Scholar
  7. 7.
    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, 1871–1874 (2008)zbMATHGoogle Scholar
  8. 8.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101(suppl 1), 5228–5235 (2004)CrossRefGoogle Scholar
  9. 9.
    Hand, D.J., Henley, W.E.: Statistical classification methods in consumer credit scoring: a review. J. Royal Stat. Soc. Ser. A (Stat. Soc.) 160(3), 523–541 (1997)CrossRefGoogle Scholar
  10. 10.
    Harris, T.: Default definition selection for credit scoring. Artif. Intell. Res. 2(4), 49 (2013)Google Scholar
  11. 11.
    Hong, L., Davison, B.D.: Empirical study of topic modeling in twitter. In: Proceedings of the First Workshop on Social Media Analytics, pp. 80–88. ACM (2010)Google Scholar
  12. 12.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: WebKDD/SNA-KDD, WebKDD/SNA-KDD 2007, pp. 56–65 (2007)Google Scholar
  13. 13.
    Jensen, H.L.: Using neural networks for credit scoring. Manag. Finance 18(6), 15–26 (1992)Google Scholar
  14. 14.
    Kruppa, J., Schwarz, A., Arminger, G., Ziegler, A.: Consumer credit risk: individual probability estimates using machine learning. Expert Syst. Appl. 40(13), 5125–5131 (2013)CrossRefGoogle Scholar
  15. 15.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, WWW, pp. 591–600 (2010)Google Scholar
  16. 16.
    Li, R., Wang, C., Chang, K.C.-C.: User profiling in an ego network: co-profiling attributes and relationships. In: Proceedings of the 23rd International Conference on World Wide Web, WWW (2014)Google Scholar
  17. 17.
    Mislove, A., Viswanath, B., Gummadi, P.K., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: WSDM, pp. 251–260 (2010)Google Scholar
  18. 18.
    Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: "How old do you think i am?" a study of language and age in twitter. In: ICWSM (2013)Google Scholar
  19. 19.
    Pennacchiotti, M., Popescu, A.-M.: Democrats, republicans and starbucks afficionados: user classification in twitter. In: KDD, pp. 430–438 (2011)Google Scholar
  20. 20.
    Qiu, M., Zhu, F., Jiang, J.: It is not just what we say, but how we say them: Lda-based behavior-topic model. In: SDM, pp. 794–802 (2013)Google Scholar
  21. 21.
    Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: SMUC, pp. 37–44 (2010)Google Scholar
  22. 22.
    Rosenthal, S., McKeown, K.: Age prediction in blogs: a study of style, content, and online behavior in pre- and post-social media generations. In: ACL, pp. 763–772 (2011)Google Scholar
  23. 23.
    Wiginton, J.C.: A note on the comparison of logit and discriminant models of consumer credit behavior. J. Financial Quant. Anal. 15(03), 757–770 (1980)CrossRefGoogle Scholar
  24. 24.
    Yap, B.W., Ong, S.H., Husain, N.H.M.: Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Syst. Appl. 38(10), 13274–13283 (2011)CrossRefGoogle Scholar
  25. 25.
    Zeng, G., Luo, P., Chen, E., Wang, M.: From social user activities to people affiliation. In: ICDM (2013)Google Scholar
  26. 26.
    Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., Li, X.: Comparing twitter and traditional media using topic models. In: ECIR, pp. 338–349 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Guangming Guo
    • 1
    • 2
  • Feida Zhu
    • 2
  • Enhong Chen
    • 1
    Email author
  • Le Wu
    • 1
  • Qi Liu
    • 1
  • Yingling Liu
    • 1
  • Minghui Qiu
    • 2
  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.School of Information SystemsSingapore Management UniversitySingaporeSingapore

Personalised recommendations