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 saudience. 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起了国际社会的广泛关注，尤其在2014年和2022年俄罗斯两次入侵乌克兰领土之后。尽管已有研究考察了这些事件相关的社交媒体动态，但跨语言维度的比较性纵向数据驱动视角仍付之阙如。本文通过一种源自DNA微阵列技术的简洁对数过表达制图方法，基于各语言基线频率对2008至2023年间28种语言中Twitter上对"乌克兰"的增量关注度进行映射，填补了这一空白。这种宏观尺度可视化不仅突显了已知事件，更揭示了任何单一语言用户群体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：一个在2014年达到峰值，另一个在2022年达到峰值，其各异的起落特征恰好映射了各国支持乌克兰的意愿（或抗拒）程度。通过捕捉局部、中观与全球尺度的关注度，我们的方法为比较不同语言、用户子群、平台乃至历史印刷语料库中的相对偏差提供了通用工具。最终，这种制图方法揭示了一个令人不安的非对称性：尽管公开数据能近似反映全球关注模式，但完整且无过滤的视角仍深藏于各大社交媒体平台的封闭专有算法之后，这使人们无法获得对全球信息流的全面理解。