title: Multi-scale compositionality: identifying the compositional structures of social dynamics using deep learning.
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.
  
  
  METHODS
  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.
authors.affiliation: Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
authors.forename: Huan-Kai
authors.initials: HK
authors.lastname: Peng
authors.affiliation: Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
authors.forename: Radu
authors.initials: R
authors.lastname: Marculescu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D000069550:Machine Learning; D012919:Social Behavior; D061108:Social Media; D013997:Time Factors
references.citation: Neural Comput. 2006 Jul;18(7):1527-54
references.pmid: 16764513
references.citation: Nature. 1996 Jun 13;381(6583):607-9
references.pmid: 8637596
references.citation: Science. 2006 Jul 28;313(5786):504-7
references.pmid: 16873662