Contrasting public, market-driven and populist news videos on Covid-19: A semi-automatic multimodal semiotic content analysis of audiovisual news reports

This paper introduces a novel method for investigating how public service, market-driven, and populist news outlets differ in their construction of ideological positions in audiovisual reporting. A semi-automatic framework of multimodal semiotic content analysis is presented that combines computational audiovisual feature detection with theory-driven manual annotation of discourse and narrative elements and shows early results concerning the construction of distinct positions on COVID-19. The method presented suggests a powerful reconceptualisation of what counts as “large-scale” analysis in multimodal video research, arguing that scale should be defined not merely by the number of videos but be explicitly related to analytical complexity, levels of abstraction, and the density of annotated units, such as shots and many other multimodal features. Based on a corpus of 107 German news reports (January–March 2022, containing around 5000 shots) from Tagesschau (public service), BILD TV (market-driven), and CompactTV (populist/right-wing), we systematically conduct shot-by-shot annotation for multimodal semiotic content analysis. The analysis identifies recurring narrative patterns across channels and uncovers how multimodal configurations materialise distinct ideological orientations. Statistical comparisons show clear cross-channel differences. Tagesschau foregrounds expert voices and largely avoids sen-sationalist framing. BILD TV emphasises emotionalised language associated with shock and “craziness”, while CompactTV amplifies oppositional actors and frequently frames COVID-19 measures in terms of “dictatorship” and “coercion”. These findings demonstrate that multimodal discourse patterns function as ideological performances, shaping whose voices are legitimised, how events are staged, and which emotions are mobilised. The study illustrates how semi-automatic multimodal analysis enables scalable, fine-grained investigation of narrative form and ideological signalling in contemporary news environments.