The massive proliferation of social media data represents a transformative opportunity for conflict studies and for tracking the proliferation and use of weaponry, as conflicts are increasingly documented in these online spaces. At the same time, the scale and types of data available are problematic for traditional open-source intelligence. This paper focuses on identifying specific weapon systems and the insignias of the armed groups using them as documented in the Ukraine war, as these tasks are critical to operational intelligence and tracking weapon proliferation, especially given the scale of international military aid given to Ukraine. The large scale of social media makes manual assessment difficult, however, so this paper presents early work that uses computer vision models to support this task. We demonstrate that these models can both identify weapons embedded in images shared in social media and how the resulting collection of military-relevant images and their post times interact with the offline, real-world conflict. Not only can we then track changes in the prevalence of images of tanks, land mines, military trucks, etc., we find correlations among time series data associated with these images and the daily fatalities in this conflict. This work shows substantial opportunity for examining similar online documentation of conflict contexts, and we also point to future avenues where computer vision can be further improved for these open-source intelligence tasks.

翻译：社交媒体数据的大规模涌现为冲突研究及武器扩散与使用追踪带来了变革性机遇，因为冲突事件日益在这些网络空间中得到记录。与此同时，现有数据的规模与类型对传统开源情报分析构成了挑战。本文聚焦于识别乌克兰战争中记录的特定武器系统及使用这些武器的武装组织标识，鉴于国际对乌军事援助的规模，这些任务对作战情报和武器扩散追踪至关重要。然而，社交媒体的庞大规模使得人工评估难以进行，因此本文提出了利用计算机视觉模型辅助该任务的初步研究成果。我们证明这些模型既能识别社交媒体分享图像中嵌入的武器，也能分析由此收集的军事相关图像及其发布时间如何与线下现实冲突产生关联。我们不仅能够追踪坦克、地雷、军用卡车等图像出现频率的变化，还发现这些图像相关的时间序列数据与冲突中日均伤亡人数之间存在相关性。本研究表明，利用类似方法考察其他冲突情境的网络记录具有广阔前景，同时我们也指出了未来可进一步改进计算机视觉技术以更好完成此类开源情报任务的研究方向。