HSSH Brown Bag Seminar: Scaling Qualitative and Narrative Analysis of Multimodal Research Materials: The Datascene

Digital research materials increasingly combine multiple forms of evidence: moving and still images, speech, sound, written language, webpages, metadata and pre-existing structured datasets. Yet computational research methods frequently analyse these materials separately or reduce them to whichever modality is easiest to process. Text may be detached from its audiovisual context, images from their surrounding discourse, and statistical patterns from the original materials through which their social meaning was produced.

This HSSH Brown Bag presentation introduces Datascene, a developing research infrastructure and computational method for analysing heterogeneous and multimodal materials as structured research data. Datascene is designed to support video, audio recordings, textual documents, individual photographs, websites and pre-existing multimodal research corpora. It brings together speech and language processing, computer vision, temporal analysis, semantic metadata, machine learning, statistical methods and computational social-science approaches.

Audiovisual material provides the project’s principal methodological case because video combines temporal, visual, auditory, textual and narrative evidence within the same source. The underlying method is nevertheless more general: observations from different materials and modalities are represented as mutually addressable, inspectable and comparable analytical objects while maintaining transparent links to their original sources.

The associated method paper asks how qualitative and narrative analysis can be computationally operationalized and scaled without detaching theoretical concepts from source evidence or transferring interpretive authority entirely to automated systems. Datascene approaches actors and agencies, events, relations, narrative developments and other theoretically informed concepts as source-bound analytical objects. Computational systems may detect, organize, compare and propose connections among evidence, while researchers retain authority to inspect, correct, confirm, reject and interpret the resulting propositions.

The presentation will discuss several dimensions of scalability: analysis across larger corpora, movement between different analytical levels, integration of multiple modalities and material types, comparison across sources, and reuse of confirmed observations across analytical procedures. It will also consider the methodological distinction:

Observation ≠ measurement ≠ candidate interpretation ≠ confirmed interpretation ≠ research claim.

The broader objective is to establish scalable and reproducible workflows for research-grade multimodal analysis while preserving human interpretability, theoretical plurality and methodological traceability. Datascene is intended to support research in the social sciences, humanities, communication, media studies and related fields, as well as the analysis of heterogeneous digital collections held by cultural and memory institutions. Future large-scale processing is planned through CSC computing infrastructure.

The session is particularly recommended for researchers interested in the use of artificial intelligence, machine learning and statistical methods in qualitative research. No specialist technical background is required. Participants are invited to consider how computational methods might extend interpretive research without displacing its theoretical foundations or the researcher’s analytical judgment.

Petteri Laine  is a PhD researcher in Computational Social Sciences with more than 20 years of experience in media, communications, and creative industry management. He combines extensive industry expertise with solid computational and analytical research skills. He is the research lead of Datascene and has a clear vision for its development. Laine is writing his thesis about the emergence of the self-determined data subject in the faculty of social sciences of the University of Helsinki.

Jukka Kortti is a social science historian at the University of Helsinki. He holds the title of docent (associate/adjunct professor) at the University of Helsinki and Aalto University. His research interests focus on media history and intellectual history. He has recently directed a research project on the history of the Finnish public broadcasting company Yle (Yle100) and has published widely on media history, including textbooks and peer-reviewed theoretical contributions in leading journals of the field. He is currently preparing a monograph on historical documentary films for Palgrave Macmillan.

Reko Elovainio is a PhD researcher in Social Psychology with more than 10 years of experience in related fields. His expertise lies in Computational Social Science methodologies and the application of virtual reality technologies in social psychological research. Elovainio has strong understanding of the challenges in managing video data.

You’ll find more information and the zoom link visit : https://www.helsinki.fi/en/helsinki-institute-social-sciences-and-humanities/events/brown-bag-seminar