Motif-Based Exploratory Data Analysis for State-Backed Platform Manipulation on Twitter

Abstract

State-backed platform manipulation (SBPM) on Twitter has been a prominent public issue since the 2016 US election cycle. Identifying and characterizing users on Twitter as belonging to a state-backed campaign is an important part of mitigating their influence. In this paper, we propose a novel time series feature grounded in social science to characterize dynamic user networks on Twitter. We introduce a classification approach, motif functional data analysis (MFDA), that captures the evolution of motifs in temporal networks, which is a useful feature for analyzing malign influence. We evaluate MFDA on data from known SBPM campaigns on Twitter and representative authentic data and compare performance to other classification methods. To further leverage our dynamic feature, we use the changes in network structure captured by motifs to help uncover real-world events using anomaly detection.