Influence Estimation and Opinion-Tracking Over Online Social Networks

This article presents a restricted maximum likelihood-based algorithm to estimate who influences whose opinions and to what degree when agents share their opinions over large online social networks such as Twitter. The proposed algorithm uses multi-core processing and distributed computing to provide a scalable solution as the optimization problems are large in scale; a network with 10,000 agents and average connectivity of 100 requires estimates of about 1 million parameters. A computational study is then used to show that the estimates are efficient and robust when the full rank conditions for the covariance matrix are met. The results also highlight the importance of the quantity of the information being shared over the social network for the inference of the influence structure.