A Call to Arms: AI Should be Critical for Social Media Analysis of Conflict Zones
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866908382164680704 |
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| author | Abedin, Afia Bais, Abdul Buntain, Cody Courchesne, Laura McQuinn, Brian Taylor, Matthew E. Ullah, Muhib |
| author_facet | Abedin, Afia Bais, Abdul Buntain, Cody Courchesne, Laura McQuinn, Brian Taylor, Matthew E. Ullah, Muhib |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_00810 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Call to Arms: AI Should be Critical for Social Media Analysis of Conflict Zones Abedin, Afia Bais, Abdul Buntain, Cody Courchesne, Laura McQuinn, Brian Taylor, Matthew E. Ullah, Muhib Computers and Society Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | A Call to Arms: AI Should be Critical for Social Media Analysis of Conflict Zones |
| topic | Computers and Society Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2311.00810 |