Abstract
User profiling in social media plays an important role in different applications. Most of the existing approaches for user profiling are based on user-generated messages, which is not sufficient for inferring user attributes. With the continuous accumulation of data in social media, integrating multi-data sources has become the inexorable trend for precise user profiling. In this paper, we take advantage of text messages, user metadata, followee information and network representations. In order to integrate seamlessly multi-data sources, we propose a novel fusion model that effectively captures the complementarity and diversity of different sources. In addition, we address the problem of friendship-based network from previous studies and introduce celebrity ties which enrich the social network and boost the connectivity of different users. Experimental results show that our method outperforms several state-of-the-art methods on a real-world dataset.
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References
Lu, Z., Pan, S.J., Li, Y., Jiang, J., Yang, Q.: Collaborative evolution for user profiling in recommender systems. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 3804–3810 (2016)
Zhou, M.: Gender difference in web search perceptions and behavior: does it vary by task performance? Comput. Educ. 78(259), 174–184 (2014)
Preoţiuc-Pietro, D., Liu, Y., Hopkins, D., Ungar, L.: Beyond binary labels: political ideology prediction of twitter users. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 729–740 (2017)
Zhang, D., Yin, J., Zhu, X., Zhang, C.: User profile preserving social network embedding. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 3378–3384 (2017)
Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating gender on Twitter. In: Conference on Empirical Methods in Natural Language Processing, pp. 1301–1309 (2011)
Chen, J., Li, S., Dai, B., Zhou, G.: Active learning for age regression in social media. In: China National Conference on Chinese Computational Linguistics, pp. 351–362 (2016)
Roller, S., Speriosu, M., Rallapalli, S., Wing, B., Baldridge, J.: Supervised text-based geolocation using language models on an adaptive grid. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1500–1510 (2012)
Preoţiuc-Pietro, D., Lampos, V., Aletras, N.: An analysis of the user occupational class through Twitter content. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 1754–1764 (2015)
Kim, H.R., Chan, P.K.: Learning implicit user interest hierarchy for context in personalization. Appl. Intell. 28(2), 153–166 (2008)
Majumder, N., Poria, S., Gelbukh, A., Cambria, E.: Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32(2), 74–79 (2017)
Lampos, V., Aletras, N.: Predicting and characterising user impact on Twitter. In: Conference of the European Chapter of the Association for Computational Linguistics, pp. 405–413 (2014)
Schler, J., Koppel, M., Argamon, S., Pennebaker, J.: Effects of age and gender on Blogging. In: Proceedings of AAAI Symposium on Computational Approaches for Analyzing Weblogs, pp. 199–205 (2006)
Ciot, M., Sonderegger, M., Ruths, D.: Gender inference of Twitter users in non-english contexts. In: Conference on Empirical Methods in Natural Language Processing, pp. 1136–1145 (2013)
Mukherjee, A., Liu, B.: Improving gender classification of blog authors. In: Conference on Empirical Methods in Natural Language Processing, pp. 158–166 (2010)
Marquardt, J., et al.: Age and gender identification in social media. In: Proceedings of CLEF 2014 Evaluation Labs, pp. 1129–1136 (2014)
Mislove, A., Viswanath, B., Gummadi, K., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Third ACM International Conference on Web Search and Data Mining, pp. 251–260 (2010)
Han, X., Wang, L., Crespi, N., Park, S., Cuevas, Á.: Alike people, alike interests? inferring interest similarity in online social networks. Decision Support Systems 69(C), 92–106 (2015)
Miura, Y., Taniguchi, M., Taniguchi, T., Ohkuma, T.: Unifying text, metadata, and user network representations with a neural network for geolocation prediction. In: Meeting of the Association for Computational Linguistics, pp. 1260–1272 (2017)
Wang, J., Li, S., Zhou, G.: Joint learning on relevant user attributes in micro-blog. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 4130–4136 (2017)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 855–864 (2016)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations bryan. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Tang, J., Qu, M.: LINE: large-scale information network embedding categories and subject descriptors. In: International World Wide Web Conferences Steering Committee, pp. 1067–1077 (2015)
Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 2111–2117 (2015)
Zhao, Z., Du, J., Gao, Q., Gui, L., Xu, R.: Inferring user profile using microblog content and friendship network. In: Communications in Computer and Information Science, pp. 29–39 (2017)
Han, B., Cook, P., Baldwin, T.: A stacking-based approach to twitter user geolocation prediction. In: Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 7–12 (2013)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Computer Science (2014)
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the International Conference on Learning Representations, pp. 1–12 (2013)
Kingma, D.P., Ba, J.L.: Adam: A method for stochastic optimization. Computer Science (2014)
Van Der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2605), 2579–2605 (2008)
Acknowledgment
This work was supported by the National Natural Science Foundation of China (No. 61572145) and the Major Projects of Guangdong Education Department for Foundation Research and Applied Research (No. 2017KZDXM031). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.
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Zhang, L., Fu, S., Jiang, S., Bao, R., Zeng, Y. (2018). A Fusion Model of Multi-data Sources for User Profiling in Social Media. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_1
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DOI: https://doi.org/10.1007/978-3-319-99501-4_1
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