Title: Discriminative Modeling of Social Influence for Prediction and Explanation in Event Cascades,
Abstract: The global dynamics of event cascades are often governed by the local
dynamics of peer influence. However, detecting social influence from
observational data is challenging, due to confounds like homophily and
practical issues like missing data. In this work, we propose a novel
discriminative method to detect influence from observational data. The core of
the approach is to train a ranking algorithm to predict the source of the next
event in a cascade, and compare its out-of-sample accuracy against a
competitive baseline which lacks access to features corresponding to social
influence. Using synthetically generated data, we provide empirical evidence
that this method correctly identifies influence in the presence of confounds,
and is robust to both missing data and misspecification --- unlike popular
alternatives. We also apply the method to two real-world datasets: (1) cascades
of co-sponsorship of legislation in the U.S. House of Representatives, on a
social network of shared campaign donors; (2) rumors about the Higgs boson
discovery, on a follower network of $10^5$ Twitter accounts. Our model
identifies the role of peer influence in these scenarios, and uses it to make
more accurate predictions about the future trajectory of cascades.