Streaming services have profoundly transformed the audiovisual industry, reshaping both production and distribution practices as well as viewing habits. Notably, video-on-demand (VoD) services have greatly expanded the number of series produced annually. Yet despite the extensive volume of audiovisual productions available on VoD services, most scholarly work continues to prioritize case studies or limit their scope to a small corpus of texts. This article critically examines artificial intelligence (AI)-assisted content analysis as a methodological avenue that could allow scholars to analyze extensive corpuses of audiovisual productions available on streaming services. Using multimodal generative algorithms and other integrated digital tools, such as the large language model (LLM) Gemini and the platform Google AI Studio, we will show how AI-assisted analysis might enable more thorough understandings of media production within the VoD landscape. Drawing on the results of test analyses conducted with Gemini, this article also critically addresses the epistemological and methodological challenges of AI-augmented content analysis.
Keywords: video-on-demand, content analysis, multimodal AI, large language models (LLMs), Gemini