Complex Visual Phenomena: Concepts and Methods for Digital Image Analysis

This session addresses the question of how complex visual phenomena of the digital present can be analyzed systematically. While classical approaches to image analysis have traditionally focused on single images, contemporary digital media are characterized by networked, serial, and platform-specific visual forms. Images increasingly appear as elements of posts, feeds, iconic image clusters (Mueller, 2024), audiovisual sequences, visual vernaculars, and digitally mediated discourses. Meaning emerges relationally through seriality, referentiality, multimodal communication, and algorithmically shaped visibility (Bucher, 2018).

From a sociological perspective, these visual formations constitute central modes of contemporary social communication. As emphasized in visual sociology (Emmison, Smith & Mayall, 2012), visual data provide insight into how actors construct meaning, negotiate identities, communicate experiences, perform belonging, and organize visibility in everyday life. They also offer access to processes of self-presentation, networked publics (boyd, 2014), collective memory, public discourse, and digital transformation.

The growing importance of such phenomena raises methodological challenges. If visual communication unfolds through image clusters, platform architectures, visual vernaculars, and audiovisual assemblages rather than isolated images, qualitative methods must account for the communicative structures through which meaning is produced, circulated, and transformed.

The session aims to discuss conceptual and methodological foundations for the qualitative analysis of visual data. Particular attention will be paid to single images, image clusters, images embedded in discursive interactions, visual vernaculars, and video and audiovisual data. We welcome contributions that introduce innovative methods, adapt established image-analytic approaches to emerging forms of digital data, or reflect on methodological challenges through empirically grounded case studies.