标题： 揭示双语信息操作中的行为差异：一种基于网络的方法 显示英文标题

标题： Unveiling Behavioral Differences in Bilingual Information Operations: A Network-Based Approach

摘要： 推特已成为进行信息操作（IOs）的关键平台，特别是在高风险的政治事件中。 在本研究中，我们分析了关于2024年美国总统选举的超过一百万条推文，以探索一个研究不足的领域：英语和西班牙语社区的信息操作驱动者的的行为差异。 使用从行为模式构建的相似性图，我们在两种语言中识别信息操作驱动者，并在无监督设置下评估这些图的聚类质量。 我们的分析展示了不同的网络拆解策略，如节点修剪和边过滤，如何影响聚类质量和协调信息操作驱动者的识别。 我们还揭示了英语和西班牙语信息操作驱动者在主题和政治指标方面的显著差异。 此外，我们研究了同时用两种语言发帖的双语用户，系统地揭示了他们与单语用户相比的不同角色和行为。 这些发现强调了建立强大、文化上和语言上适应性强的信息操作检测方法的重要性，以减轻社交媒体上的影响力活动的风险。 我们的代码和数据可在GitHub上获取：https://github.com/bowenyi-pierre/humans-lab-hackathon-24. 显示英文摘要

摘要： 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.