Abstract
With the rapid development of more and more social media applications, lots of users are connected with friends and their daily life and opinions are recorded. Social media provides us an unprecedented way to collect and analyze billions of users’ information. Proper user attribute identification or profile inference becomes more and more attractive and feasible. However, the flourishing social records also pose great challenge in effective feature selection and integration for user profile inference. This is mainly caused by the text sparsity and complex community structures.
In this paper, we propose a comprehensive framework to infer user’s occupation from his/her social activities recorded in micro-blog message streams. A multi-source integrated classification model is set up with some fine selected features. We first identify some beneficial basic content features, and then we proceed to tailor a community discovery based latent dimension solution to extract community features.
Extensive empirical studies are conducted on a large real micro-blog dataset. Not only we demonstrate the integrated model shows advantages over several baseline methods, but also we verify the effect of homophily in users’ interaction records. The different effects of heterogeneous interactive networks are also revealed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Backstrom, L., Sun, E., Marlow, C.: Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th International Conference on World Wide Web, pp. 61–70. ACM (2010)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in twitter: The million follower fallacy. In: 4th International AAAI Conference on Weblogs and Social Media (ICWSM), vol. 14, page 8 (2010)
Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Physical Review E 70(6), 066111 (2004)
Conover, M.D., Ratkiewicz, J., Francisco, M., Gonçalves, B., Flammini, A., Menczer, F.: Political polarization on twitter. In: Proc. 5th Intl. Conference on Weblogs and Social Media (2011)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)
Hagen, L., Kahng, A.B.: New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 11(9), 1074–1085 (1992)
Han, J.: Mining heterogeneous information networks by exploring the power of links. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 13–30. Springer, Heidelberg (2009)
Hofmann, T.: Probabilistic latent semantic analysis. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 289–296. Morgan Kaufmann Publishers Inc. (1999)
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 the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1023–1031. ACM (2012)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology, 415–444 (2001)
Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 251–260. ACM (2010)
Newman, M.E.: Finding community structure in networks using the eigenvectors of matrices. Physical Review E 74(3), 036104 (2006)
Newman, M.E.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)
Otterbacher, J.: Inferring gender of movie reviewers: exploiting writing style, content and metadata. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 369–378. ACM (2010)
Pennacchiotti, M., Popescu, A.-M.: Democrats, republicans and starbucks afficionados: user classification in twitter. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 430–438. ACM (2011)
Pothen, A., Simon, H.D., Liou, K.-P.: Partitioning sparse matrices with eigenvectors of graphs. SIAM Journal on Matrix Analysis and Applications 11(3), 430–452 (1990)
Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents, pp. 37–44. ACM (2010)
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29(3), 93 (2008)
Sun, Y., Norick, B., Han, J., Yan, X., Yu, P.S., Yu, X.: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1348–1356. ACM (2012)
Tang, L., Liu, H.: Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 817–826. ACM (2009)
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 the 20th International Conference on World Wide Web, pp. 537–546. ACM (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, X., Yu, L., Yao, J., Cui, B. (2013). A Multiple Feature Integration Model to Infer Occupation from Social Media Records. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41154-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-41154-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41153-3
Online ISBN: 978-3-642-41154-0
eBook Packages: Computer ScienceComputer Science (R0)