Leveraging social media to engineer success
From Facebook to Twitter, Yelp to Mashable—social media channels
Imagine you are scrolling through your Facebook feed and notice the local news station posts an alert about a traffic accident located
In their recent paper, “Data-driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization,” recently published in PLoS One, researchers aim at finding the best short-term interventions that can lead to predefined long-term outcomes.
“The goal of this work is to find what the best intervention dynamics would look like given the input data,” says Radu Marculescu, ECE professor at Carnegie Mellon and contributor to the paper. “In other words, society could actually implement a particular intervention in social media that results in a predicted outcome.”
Abstract
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. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit