Abstract
User occupation plays an important role in many applications such as personalized recommendation and targeted advertising. However, user occupations on microblogging platforms are usually unavailable to public due to personal privacy. This opens an interesting problem, i.e., how to predict user occupations on microblogging platforms. In this paper, we propose a framework for extracting user occupations on microblogs. In particular, we implement a number of classification models and devise various sets of features for predicting user occupations, and devise an occupation-oriented lexicon to generate the training data. The experimental results show that the proposed lexicon-based method can achieve higher accuracy compared with traditional models.
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References
Pennacchiotti, M., Popescu, A.: A machine learning approach to Twitter user classification. In: ICWSM, pp. 281–288 (2011)
Zheng, L., Jin, P., Zhao, J., Yue, L.: A fine-grained approach for extracting events on microblogs. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds.) DEXA 2014, Part I. LNCS, vol. 8644, pp. 275–283. Springer, Heidelberg (2014)
Zhang, Q., Jin, P., Lin, S., Yue, L.: Extracting focused locations for web pages. In: Wang, L., Jiang, J., Lu, J., Hong, L., Liu, B. (eds.) WAIM 2011. LNCS, vol. 7142, pp. 76–89. Springer, Heidelberg (2012)
Jin, P., Lian, J., Zhao, X., Wan, S.: TISE: a temporal search engine for web content. In: IITA, vol. 3, pp. 220–224 (2008)
Huang, Y., Yu, L., Wang, X., Cui, B.: A multi-source integration framework for user occupation inference in social media systems. World Wide Web 18(5), 1247–1267 (2015)
Preoţiuc-Pietro, D., Lampos, V., Aletras, N.: An analysis of the user occupational class through Twitter content. In: ACL, pp. 1754–1764 (2015)
Tu, C., Liu, Z., Sun, M.: PRISM: profession identification in social media with personal information and community structure. In: Zhang, X., Sun, M., Wang, Z., Huang, X. (eds.) SMP 2015. CCIS, vol. 568, pp. 15–27. Springer, Heidelberg (2015)
Yan, X., Guo, J., Lan, Y., et al.: A biterm topic model for short texts. In: WWW, pp. 1445–1456 (2013)
Acknowledgements
This work is supported by the National Science Foundation of China (61379037 and 71273010).
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Lv, X., Jin, P., Yue, L. (2016). User Occupation Prediction on Microblogs. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_54
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DOI: https://doi.org/10.1007/978-3-319-45817-5_54
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