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. • We introduce an operationalization to detect multimodal coordinated behavior, which fully exploits the potential of the interdependence among different modalities (e.g., co-retweets, co-hashtags, co-URLs), by exploiting a multiplex coordination network. • We analyze the contributions of different monomodal approaches in detecting coordinated online behavior, revealing whether various data modalities provide complementary or overlapping insights. • We compare different levels of multimodal integration, highlighting the trade-offs between broadly capturing coordination patterns and strictly identifying tightly coordinated users, providing guidance for robust multimodal analyses.
Mannocci et al. (Sat,) studied this question.