Twitter has become a pivotal platform for conducting information operations (IOs), particularly during high-stakes political events. In this study, we analyze over a million tweets about the 2024 U.S. presidential election to explore an under-studied area: the behavioral differences of IO drivers from English- and Spanish-speaking communities. Using similarity graphs constructed from behavioral patterns, we identify IO drivers in both languages and evaluate the clustering quality of these graphs in an unsupervised setting. Our analysis demonstrates how different network dismantling strategies, such as node pruning and edge filtering, can impact clustering quality and the identification of coordinated IO drivers. We also reveal significant differences in the topics and political indicators between English and Spanish IO drivers. Additionally, we investigate bilingual users who post in both languages, systematically uncovering their distinct roles and behaviors compared to monolingual users. These findings underscore the importance of robust, culturally and linguistically adaptable IO detection methods to mitigate the risks of influence campaigns on social media. Our code and data are available on GitHub: https://github.com/bowenyi-pierre/humans-lab-hackathon-24.