Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization.

Multi-scale compositionality: identifying the compositional structures of social dynamics using deep learning.

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.