Aggression against Ukraine has drawn widespread international attention, particularly in the wake of the two Russian invasions into Ukrainian territory in 2014 and 2022. Although previous studies have examined social-media dynamics around these events, a comparative longitudinal data-driven view across languages is still missing. This article fills this gap by mapping added attention to "Ukraine" on Twitter in 28 languages from 2008 to 2023, using a deceptively simple DNA microarray-inspired cartography of log over-expression relative to each language's baseline frequency. This macro-scale visualization makes familiar events stand out while uncovering subtler patterns beyond the cognitive reach of any single-language audience. Most strikingly, two nearly non-overlapping language clusters emerge, one peaking around 2014 and the other around 2022 with distinct onset and decay profiles that mirror national readiness (or reluctance) to support Ukraine. By capturing attention at local, meso, and global scales, our approach offers a versatile tool for comparing relative bias across languages, user subgroups, platforms, or even historical print corpora. Ultimately, our cartographic approach reveals a troubling asymmetry: while publicly accessible data allows for an approximation of global attention patterns, the complete and unfiltered view remains largely hidden behind the closed, proprietary algorithms of major social media platforms, granting a far more comprehensive access to understanding global information flows.

翻译：针对乌克兰的侵略行为引发了广泛的国际关注，尤其是2014年与2022年两次俄罗斯入侵乌克兰领土的事件。尽管已有研究考察了这些事件相关的社交媒体动态，但缺乏跨语言的纵向数据驱动比较视角。本文通过构建一份基于DNA微阵列启发的简易制图方法——以每种语言基线频率为基准的对数过表达映射，填补了这一空白，系统呈现了2008年至2023年间28种语言中推特上对"乌克兰"的额外关注。这种宏观可视化使熟悉的事件得以凸显，同时揭示了任何单语言受众认知范围之外的微妙模式。最引人注目的是，出现了两个几乎不重叠的语言集群：一个集群在2014年达到峰值，另一个在2022年达到峰值，两者具有截然不同的起始与衰减特征，恰好反映了各国支持乌克兰的意愿（或抵触情绪）。通过捕捉本地、中观与全球尺度的注意力，我们的方法为比较不同语言、用户子群、平台甚至历史印刷语料库中的相对偏差提供了通用工具。最终，我们的制图方法揭示了一种令人不安的不对称性：尽管公开可访问的数据能近似反映全球注意力模式，但完整而未经过滤的视角仍主要隐藏在大型社交媒体平台封闭、专有的算法之后，从而限制了更全面理解全球信息流动的途径。