Listening for Ideology: Automatic Analysis of Character Speech in Historical Nazi Propaganda Films

Nicolas Ruth, Manuel Burghardt, Andreas Niekler


Abstract
While the visual dimension of film has been widely explored in digital humanities through methods such as “distant viewing”, the audio layer has received less attention despite its crucial role in meaning-making. We address this gap with a four-step pipeline combining speaker diarization, audio gender classification, automatic speech recognition (ASR), and LLM-based psycholinguistic analysis to infer character traits from film dialogues. Applying this method to a set of Nazi propaganda films, we find that despite challenges in speaker diarization due to noisy historical film audio, modern ASR and GPT-based analyses produce character profiles consistent with existing filmic research. Our proposed pipeline advances distant reading of film dialogue, complementing visual analyses and enabling scalable study of ideology in historical cinema. A case study of female characters in NS films identifies three recurring types, centered on the ideological figure of the mother in National Socialism.
Anthology ID:
2026.lrec-1.461
Volume:
Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:
May
Year:
2026
Address:
Palma de Mallorca, Spain
Editors:
Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:
LREC
SIG:
Publisher:
ELRA Language Resource Association
Note:
Pages:
5828–5838
Language:
External URL:
https://lrec.elra.info/lrec2026-main-461
DOI:
10.63317/4fxfeqysxtnz
Bibkey:
Cite (ACL):
Nicolas Ruth, Manuel Burghardt, and Andreas Niekler. 2026. Listening for Ideology: Automatic Analysis of Character Speech in Historical Nazi Propaganda Films. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 5828–5838, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):
Listening for Ideology: Automatic Analysis of Character Speech in Historical Nazi Propaganda Films (Ruth et al., LREC 2026)
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