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Combining machine automation with human expertise and reflexivity, the methodological framework connects computer vision and social research. The approach integrates inductive, deductive, and abductive logics and unfolds in three complementary steps: pattern exploration via unsupervised learning, theory-driven classification using convolutional neural networks, and context-sensitive interpretation. Empirical examples from digital diplomacy support critical discussion of epistemological implications.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/with-eyes-of-a-machine-a-three-step-guide-for-applying-machine-learning-to-visual-content-analysis-in-social-research/290799/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/with-eyes-of-a-machine-a-three-step-guide-for-applying-machine-learning-to-visual-content-analysis-in-social-research/290799.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What three-step framework does the paper propose for visual content analysis?","Question",{"text":113,"@type":114},"It proposes (a) pattern exploration using unsupervised learning, (b) theory-driven image classification using supervised learning with convolutional neural networks, and (c) context-sensitive interpretation of identified patterns.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the guide combine machine learning with human expertise?",{"text":118,"@type":114},"It blends machine automation with human expertise and reflexivity, emphasizing methodological rigor and critical engagement rather than treating ML as a neutral tool.",{"name":120,"@type":111,"acceptedAnswer":121},"What study examples are used to illustrate the approach?",{"text":122,"@type":114},"The paper illustrates the steps through empirical examples from a study of visuality in digital diplomacy.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290799,1789642587,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Original research article  \nWith eyes of a machine: A three-step guide for applying machine learning to visual content analysis in social research  \nAnna Helene Kvist Møller 1  and Massimo Airoldi2   \nBig Data & Society April–June: 1–13 © The Author(s) 2025 Article reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/20539517251343860](DOI: 10.1177/20539517251343860)[ ](DOI: 10.1177/20539517251343860)[journals.sagepub.com/home/bds](journals.sagepub.com/home/bds)  \nAbstract  \nThis paper presents a practical guide to machine learning–assisted visual content analysis for social scientists. Combining machine automation with human expertise and reflexivity, the proposed methodological framework bridges the gap between computer vision and social research. Our custom approach combines inductive, deductive, and abductive logics ofscientific inquiry and consists of three complementary steps: (a) Pattern exploration—employing unsupervised learning to explore visual patterns within image datasets; (b) Theory-driven image classification—utilizing supervised learning with convolutional neural networks to systematically label visual content; and (c) Context-sensitive interpretation—to provide critical and creative engagement with the patterns identified in the previous steps. We illustrate these three steps, and their various combinations, through empirical examples from a study of visuality in digital diplomacy, and critically discuss the epistemological implications of using machine learning as a method in visual social research.  \nKeywords  \nComputer vision, visual content analysis, AI, visual methods, machine learning, mixed methods  \nIntroduction  \nThe booming digital circulation of images, together with recent advancements in computational methods and AI research, has opened uncharted avenues for exploring and understanding the social world through its visual traces. The current abundance of both human- and AI-generated visual content covers every imaginable political, social, or cultural topic. This content takes many forms: from visual media shared on platforms such as Instagram, Reddit, Facebook, or TikTok, to satellite and drone imagery, CCTV footage, news visuals, and archival or historical visual records, among other types of image data. This marks a potentially unprecedented—and yet, largely underappreciated—shift for social science inquiry. However, the epistemological complexities linked, on the one hand, to the analysis of large unstructured visual datasets and, on the other hand, to their interpretation in light of the sociotechnical contexts from which they can be extracted, pose significant technical and heuristic challenges, making it difficult for social scientists to fully embrace the opportunities for knowledge gain in image data (Chen et al., 2023; Pearce et al., 2020) . This article contributes to the systematization, formalization, and diffusion of this cross-disciplinary and mostly tacit methodological knowledge, through a practical  \nguide for applying machine learning (ML) methods to visual content analysis in social research.  \nThe methodological framework guiding this work lies atthe intersection of multiple research streams centered on visual content analysis for the social sciences, including computational approaches (Joo and Steinert-Threlkeld, 2022), qualitative visual methodologies (Luhtakallio, 2013; Rose, 2016), and digital methods approaches to visual media analysis (Rogers, 2021) . In addition, drawing inspiration from methodological and analytical frameworks developed for the computational analysis of large textdata (Carlsen and Ralund, 2022; DiMaggio et al., 2013; Nelson, 2020), our practical guide attempts to combine the computational capabilities of machines with human  \n1 Copenhagen Center for Social Data Science (SODAS), University of Copenhagen, Kobenhavn, Denmark  \n2Department of Social and Political Sciences, Univ","cbCaidoBAREy55Cy","https://ap.wps.com/l/cbCaidoBAREy55Cy","pdf",791264,13,"English","# Abstract\n# Keywords\n# Introduction\n## Methodological framework and motivation","[{\"question\":\"What three-step framework does the paper propose for visual content analysis?\",\"answer\":\"It proposes (a) pattern exploration using unsupervised learning, (b) theory-driven image classification using supervised learning with convolutional neural networks, and (c) context-sensitive interpretation of identified patterns.\"},{\"question\":\"How does the guide combine machine learning with human expertise?\",\"answer\":\"It blends machine automation with human expertise and reflexivity, emphasizing methodological rigor and critical engagement rather than treating ML as a neutral tool.\"},{\"question\":\"What study examples are used to illustrate the approach?\",\"answer\":\"The paper illustrates the steps through empirical examples from a study of visuality in digital diplomacy.\"}]","With eyes of a machine - A three-step guide for applying machine learning to visual content analysis in social research | PDF",33]