Motif-Based Exploratory Analysis of State-Backed Platform Manipulation on Twitter

• Activity Number: 440 - Contributed Poster Presentations: Section on Statistics in Defense and National Security
• Type: Contributed
• Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Defense and National Security
• Author(s): Khuzaima Hameed* and Rob Johnston and Brent Younce and Minh Tang and Alyson G Wilson
• Companies: North Carolina State University and Laboratory for Analytic Sciences, North Carolina State University and Laboratory for Analytic Sciences, North Carolina State University and North Carolina State University and North Carolina State University
• Keywords: network analysis; graph embedding; time series; Twitter; classification; anomaly detection

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 presentation, we discuss a novel time series feature, based in social science, to study dynamic user networks on Twitter. We first present 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. Lastly, we use our dynamic feature in anomaly detection, where we use the changes in network structure captured by motifs to help uncover real-world events.