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Protest images in news media play a crucial role in shaping audience perceptions of social movements and influencing their subsequent participation. Despite this salient role, scholars have traditionally prioritized text analysis in their studies of news coverage of protests. We still lack a theoretical understanding of the large-scale visual representation of protests in global society. Drawing on 2.37 million protest images from over 170 countries and leveraging natural language processing and computer vision techniques, we systematically examine the aesthetic features, content, and the media-level and country-level factors that shape visual representation. Our computational analyses, including aesthetic, vision, and facial recognition, reveal substantial variation in how movements are visually depicted based on their aesthetic characteristics (e.g., color, brightness) and content (e.g., police, violence, demographics, emotions). Further we explain the variation in visual representation of movements with variables from media studies, social movement studies, world society theory, world systems theory, and international relations. Results suggest that economic and market forces shape the media to cover protests to be less colorful, darker, chaotic, and emphasize on police presence. On the contrary, strong civil society and embeddedness in global civil society leads to more colorful and brighter representations. In such cases, the media emphasizes the demands of the dissidents rather than disruption and chaos. Once included, participants are more likely to be represented as angry and happy. Lastly, the media covers external protests to be darker, less colorful, and more chaotic as compared to domestic protests. These findings contribute to developing a hierarchical model of visual representation in protest coverage, offering new insights into the intersection of media, politics, and social movements.