Structure and Context of Retweet Coordination in the 2022 U.S. Midterm Elections

TL;DR

This paper introduces a novel latent sharing-space model to detect and analyze coordinated retweet activity during the 2022 US midterm elections, revealing distinct groups and motivations behind coordination.

Contribution

It proposes a new method using a latent sharing-space model with a k-nearest neighbor threshold to identify coordination in social media data, addressing limitations of arbitrary thresholds.

Findings

Identified two main categories of coordination: entertainment promotion and political mobilization.

Discovered natural thresholds in association value distributions for detecting coordination.

Revealed common motivations behind fragmented coordinated groups.

Abstract

The ability to detect coordinated activity in communication networks is an ongoing challenge. Prior approaches emphasize considering any activity exceeding a specific threshold of similarity to be coordinated. However, identifying such a threshold is often arbitrary and can be difficult to distinguish from grassroots organized behavior. In this paper, we investigate a set of Twitter retweeting data collected around the 2022 US midterm elections, using a latent sharing-space model, in which we identify the main components of an association network, thresholded with a k-nearest neighbor criterion. This approach identifies a distribution of association values with different roles in the network at different ranges, where the shape of the distribution suggests a natural place to threshold for coordinated user candidates. We find coordination candidates belonging to two broad categories, one involving music awards and promotion of Korean pop or Taylor Swift, the other being users engaged in political mobilization. In addition, the latent space suggests common motivations for different coordinated groups otherwise fragmented by using an appropriately high threshold criterion for coordination.