Abstract
With the rapid development of microblogs in recent years, accurate prediction of microblog user profiles is valuable for marketing, personalized recommendation, and legal investigation. Microblog users post rich contents everyday and build a complex friendship network with “following” behaviors. Both of user-generated content and friendship network are crucial for user profiling. In this work, we propose a neural-network based model for user profiling. It takes advantages of both user-generated content and friendship network with attentional multi-scale convolutional neural networks and graph embeddings. We evaluate our model on SMP CUP 2016 dataset whose task is to infer age, gender and region of microblog users. The experiment results show that utilizing information from user generated content and friend network, our method obtains the state-of-the-art performance on all of three sub-tasks.
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Notes
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As training data is insufficient, the model is difficult to learn how to map a specific location that shows in user-generated content to its belonging region. Hence, we construct a region dictionary using geography knowledge and Sina Weibo location information to help our model find the relation between location and region.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China 61370165, U1636103, 61632011, Shenzhen Foundational Research Funding JCYJ20150625142543470, JCYJ20170307150024907 and Guangdong Provincial Engineering Technology Research Center for Data Science 2016KF09.
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Zhao, Z., Du, J., Gao, Q., Gui, L., Xu, R. (2017). Inferring User Profile Using Microblog Content and Friendship Network. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_3
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