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Deep Learning in Social Computing

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Deep Learning in Natural Language Processing

Abstract

The goal of social computing is to devise computational systems to learn mechanisms and principles to explain and understand the behaviors of each individual and collective teams, communities, and organizations. The unprecedented online data in social media provides a fruitful resource for this purpose. However, traditional techniques have a hard time in handling the complex and heterogeneous nature of social media for social computing. Fortunately, the recent revival and success of deep learning brings new opportunities and solutions to address these challenges. This chapter introduces the recent progress of deep learning on social computing in three aspects, namely user-generated content, social connections, and recommendation, which have covered most of the core elements and applications in social computing. Our focus lies in the discussions on how to adapt deep learning techniques to mainstream social computing tasks.

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Notes

  1. 1.

    Other data types such as images and videos are not considered, which are beyond the scope of this chapter.

  2. 2.

    http://www.epinionglobal.com/.

  3. 3.

    In our case, a vertex corresponds to a user, and the graph corresponds to the user network. Unless specified, we will use “network” for short instead of “user network”, since our methods are general and can be essentially applied to any networks of other types.

  4. 4.

    Strictly speaking, the embedding based models are not standard neural networks, such as word2vec (Mikolov et al. 2013).

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Authors and Affiliations

  1. Renmin University of China, Beijing, China

    Xin Zhao

  2. Wuhan University, Wuhan, China

    Chenliang Li

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Correspondence to Xin Zhao .

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  1. AI Research at Citadel , Chicago, Illinois, USA

    Li Deng

  2. Tsinghua University , Beijing, China

    Yang Liu

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Zhao, X., Li, C. (2018). Deep Learning in Social Computing. In: Deng, L., Liu, Y. (eds) Deep Learning in Natural Language Processing. Springer, Singapore. https://doi.org/10.1007/978-981-10-5209-5_9

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