Methodology of Decentralized Curatorial Documentary Based on Multimodal Data Analysis (UGC)

Ayala Roytman

Citation: Ayala Roytman, "Methodology of Decentralized Curatorial Documentary Based on Multimodal Data Analysis (UGC)", Universal Library of Business and Economics, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This methodological guide presents a comprehensive framework for decentralized curatorial documentary filmmaking based on multimodal analysis of User Generated Content (UGC). The study investigates the transformation of documentary language in the era of digital data redundancy, proposing an open-source editing method that restructures the traditional relationship between filmmaker and subject, replacing direct interaction with the collection, verification, contextualization, and analysis of edited, publicly available digital traces. The object of the research is contemporary documentary content created from UGC sources; the subject is the methodology of constructing screen portraits and historical chronicles without direct contact between the filmmaker and the filmed subject. The purpose of this work is to articulate and exemplify the Guilt Free Montage method as a procedural framework for constructing documentary portraits and event chronicles from publicly available User Generated Content. The method is examined as a way to reduce selected forms of interpersonal reactivity associated with direct filming, interviewing, and prolonged contact between filmmaker and subject. Rather than claiming access to an unmediated truth, the guide defines authenticity as a verifiable relation between public self representation, external observation, social resonance, and documented source provenance. Drawing on case studies-including the rapper Husky portrait episode and the Yellow Vests protests in France (2018)-the guide systematizes principles of objective distancing, source polyphony, emotional reconstruction, and event mosaics. The guide further examines AI-powered content discovery tools, OSINT verification techniques, and the concept of “Open-Source Cinema,” wherein every fragment is traceable to its publicly available original. This guide will be of interest to documentary filmmakers, investigative journalists, media scholars, digital archivists, and new media practitioners seeking data-driven approaches to non-fiction storytelling.


Keywords: User Generated Content, Decentralized Documentary, Curatorial Montage, OSINT Cinema, Multimodal Analysis, Observer Effect Elimination, Open-Source Verification, Digital Archives, Non-Invasive Filmmaking, Data-Driven Documentary.

Download doi https://doi.org/10.70315/uloap.ulbec.2025.0204018