Skip to main content

PRISM: Profession Identification in Social Media with Personal Information and Community Structure

  • Conference paper
  • First Online:
Social Media Processing (SMP 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 568))

Included in the following conference series:


User profession plays an important role in commercial services such as personalized recommendation and targeted advertising. In practice, profession information is usually unavailable due to privacy and other reasons. In this paper, we explore the task of identifying user professions according to their behaviors in social media. The task confronts the following challenges which make it non-trivial: how to incorporate heterogeneous information of user behaviors, how to effectively utilize both labeled and unlabeled data, and how to exploit community structure. To address these challenges, we present a framework of PRofession Identification in Social Media (PRISM). It takes advantages of both personal information and community structure of users in the following aspects: (1) We present a cascaded two-level classifier with heterogeneous personal features to measure the confidences of users belonging to different professions. (2) We present a multi-training process to take advantages of both labeled and unlabeled data to enhance classification performance. (3) We design a profession identification method synthetically considering the confidences from personal features and community structure. We collect a real-world dataset to conduct experiments, and experimental results demonstrate significant effectiveness of our method compared with other baseline methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. 1.

    In this paper, we use the Java version of Liblinear, developed by Benedikt Waldvogel, which can be accessed via

  2. 2.

    We select LibSVM [3] as the implementation of SVM, which can be accessed via

  3. 3.


  1. Antoniades, D., Polakis, I., Kontaxis, G., Athanasopoulos, E., Ioannidis, S., Markatos, E.P., Karagiannis, T.: we.b: the web of short URLs. In: Proceedings of WWW, pp. 715–724 (2011)

    Google Scholar 

  2. Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating gender on twitter. In: Proceedings of EMNLP, pp. 1301–1309 (2011)

    Google Scholar 

  3. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM TIST 2(3), 27 (2011)

    Google Scholar 

  4. Chaudhari, G., Avadhanula, V., Sarawagi, S.: A few good predictions: selective node labeling in a social network. In: Proceedings of WSDM, pp. 353–362 (2014)

    Google Scholar 

  5. Danescu-Niculescu-Mizil, C., Lee, L., Pang, B., Kleinberg, J.: Echoes of power: language effects and power differences in social interaction. In: Proceedings of WWW, pp. 699–708 (2012)

    Google Scholar 

  6. Dodds, P.S., Harris, K.D., Kloumann, I.M., Bliss, C.A., Danforth, C.M.: Temporal patterns of happiness and information in a global social network: hedonometrics and twitter. PLoS ONE 6(12), e26752 (2011)

    Article  Google Scholar 

  7. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. JMLR 9, 1871–1874 (2008)

    MATH  Google Scholar 

  8. Feng, W., Wang, J.: Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: Proceedings of KDD, pp. 1276–1284 (2012)

    Google Scholar 

  9. Fink, C., Kopecky, J., Morawski, M.: Inferring gender from the content of tweets: a region specific example. In: Proceedings of ICWSM (2012)

    Google Scholar 

  10. Forman, G.: An extensive empirical study of feature selection metrics for text classification. JMLR 3, 1289–1305 (2003)

    MATH  Google Scholar 

  11. Golbeck, J., Robles, C., Turner, K.: Predicting personality with social media. In: Proceedings of CHI, pp. 253–262 (2011)

    Google Scholar 

  12. Goswami, S., Sarkar, S., Rustagi, M.: Stylometric analysis of bloggers’ age and gender. In: Proceedings of ICWSM (2009)

    Google Scholar 

  13. Jacob, Y., Denoyer, L., Gallinari, P.: Learning latent representations of nodes for classifying in heterogeneous social networks. In: Proceedings WSDM, pp. 373–382 (2014)

    Google Scholar 

  14. Kong, X., Cao, B., Yu, P.S.: Multi-label classification by mining label and instance correlations from heterogeneous information networks. In: Proceedings of KDD, pp. 614–622 (2013)

    Google Scholar 

  15. Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.C.: Towards social user profiling: unified and discriminative influence model for inferring home locations. In: Proceedings of KDD, pp. 1023–1031 (2012)

    Google Scholar 

  16. Liu, Z., Tu, C., Sun, M.: Tag dispatch model with social network regularization for microblog user tag suggestion. In: Proceedings of COLING (2012)

    Google Scholar 

  17. McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27, 415–444 (2001)

    Article  Google Scholar 

  18. Mislove, A., Lehmann, S., Ahn, Y.Y., Onnela, J.P., Rosenquist, J.N.: Understanding the demographics of twitter users. In: Proceedings of ICWSM (2011)

    Google Scholar 

  19. Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of WSDM, pp. 251–260 (2010)

    Google Scholar 

  20. Newman, M.E.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)

    Article  Google Scholar 

  21. Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: Proceedings of Workshop on Search and Mining User-Generated Contents, pp. 37–44 (2010)

    Google Scholar 

  22. Sachan, M., Dubey, A., Srivastava, S., Xing, E.P., Hovy, E.: Spatial compactness meets topical consistency: jointly modeling links and content for community detection. In: Proceedings of WSDM, pp. 503–512 (2014)

    Google Scholar 

  23. Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Ramones, S.M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M.E., et al.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS ONE 8(9), e73791 (2013)

    Article  Google Scholar 

  24. Volti, R.: An Introduction to the Sociology of Work and Occupations. Pine Forge Press, Thousand Oaks (2011)

    Google Scholar 

  25. Yang, S.H., Long, B., Smola, A., Sadagopan, N., Zheng, Z., Zha, H.: Like like alike: joint friendship and interest propagation in social networks. In: Proceedings of WWW, pp. 537–546 (2011)

    Google Scholar 

  26. Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. Proc. ICML 97, 412–420 (1997)

    Google Scholar 

  27. Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3(1), 1–130 (2009)

    Article  MATH  Google Scholar 

Download references


This work is supported by the National Natural Science Foundation of China under Grant Nos. 61170196 and 61202140 and the Major Project of the National Social Science Foundation of China under Grant No. 13&ZD190.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Zhiyuan Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media Singapore

About this paper

Cite this paper

Tu, C., Liu, Z., Sun, M. (2015). PRISM: Profession Identification in Social Media with Personal Information and Community Structure. In: Zhang, X., Sun, M., Wang, Z., Huang, X. (eds) Social Media Processing. SMP 2015. Communications in Computer and Information Science, vol 568. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0079-9

  • Online ISBN: 978-981-10-0080-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics