Multimodal Coordinated Online Behavior: Trade-offs and Strategies

TL;DR

This paper compares different multimodal approaches to detecting coordinated online behavior, highlighting that multimodal analysis provides more comprehensive insights than monomodal methods, thereby improving detection of complex coordination patterns.

Contribution

It systematically evaluates various strategies for operationalizing multimodal coordination, revealing the advantages of integrated multimodal analysis over monomodal and flattened approaches.

Findings

Multimodal analysis captures more coordination structures.

Not all modalities contribute equally to insights.

Integrated models outperform monomodal methods.

Abstract

Coordinated online behavior, which spans from beneficial collective actions to harmful manipulation such as disinformation campaigns, has become a key focus in digital ecosystem analysis. Traditional methods often rely on monomodal approaches, focusing on single types of interactions like co-retweets or co-hashtags, or consider multiple modalities independently of each other. However, these approaches may overlook the complex dynamics inherent in multimodal coordination. This study compares different ways of operationalizing multimodal coordinated behavior, examining the trade-off between weakly and strongly integrated models and their ability to capture broad versus tightly aligned coordination patterns. By contrasting monomodal, flattened, and multimodal methods, we evaluate the distinct contributions of each modality and the impact of different integration strategies. Our findings show that while not all modalities provide unique insights, multimodal analysis consistently offers a more informative representation of coordinated behavior, preserving structures that monomodal and flattened approaches often lose. This work enhances the ability to detect and analyze coordinated online behavior, offering new perspectives for safeguarding the integrity of digital platforms.