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Multi-Scale Compositionality: Identifying the Compositional Structures of Social Dynamics Using Deep Learning

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  • Huan-Kai Peng
  • Radu Marculescu

Abstract

Objective: Social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. In order to study this complex dynamics, two fundamental questions revolve around (1) the signatures of social dynamics at different time scales, and (2) the way in which these signatures interact and form higher-level meanings. Method: In this paper, we propose the Recursive Convolutional Bayesian Model (RCBM) to address both of these fundamental questions. The key idea behind our approach consists of constructing a deep-learning framework using specialized convolution operators that are designed to exploit the inherent heterogeneity of social dynamics. RCBM’s runtime and convergence properties are guaranteed by formal analyses. Results: Experimental results show that the proposed method outperforms the state-of-the-art approaches both in terms of solution quality and computational efficiency. Indeed, by applying the proposed method on two social network datasets, Twitter and Yelp, we are able to identify the compositional structures that can accurately characterize the complex social dynamics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. Finally, our analysis offers new insights on understanding and engineering social media dynamics, with direct applications to opinion spreading and online content promotion.

Suggested Citation

  • Huan-Kai Peng & Radu Marculescu, 2015. "Multi-Scale Compositionality: Identifying the Compositional Structures of Social Dynamics Using Deep Learning," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-28, April.
  • Handle: RePEc:plo:pone00:0118309
    DOI: 10.1371/journal.pone.0118309
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    References listed on IDEAS

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    1. Bernard J. Jansen & Mimi Zhang & Kate Sobel & Abdur Chowdury, 2009. "Twitter power: Tweets as electronic word of mouth," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2169-2188, November.
    2. Mor Naaman & Hila Becker & Luis Gravano, 2011. "Hip and trendy: Characterizing emerging trends on Twitter," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(5), pages 902-918, May.
    3. Mor Naaman & Hila Becker & Luis Gravano, 2011. "Hip and trendy: Characterizing emerging trends on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(5), pages 902-918, May.
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    Cited by:

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    2. Keer Yang & Guanqun Zhang & Chuan Bi & Qiang Guan & Hailu Xu & Shuai Xu, 2023. "Improving CNN-base Stock Trading By Considering Data Heterogeneity and Burst," Papers 2303.09407, arXiv.org.
    3. Yuanyuan Yang & Md Muhie Menul Haque & Dongling Bai & Wei Tang, 2021. "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review," Energies, MDPI, vol. 14(21), pages 1-26, October.
    4. Xiaorui Shao & Chang-Soo Kim & Palash Sontakke, 2020. "Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN–LSTM," Energies, MDPI, vol. 13(8), pages 1-22, April.

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