AI Blob! LLM-Driven Recontextualization of Italian Television Archives

TL;DR

AI Blob! leverages LLMs, semantic embeddings, and retrieval techniques to enable dynamic, thematic recontextualization and narrative construction of Italian television archives, fostering innovative engagement with audiovisual content.

Contribution

Introduces AI Blob!, a novel system combining LLMs and semantic retrieval for recontextualizing television archives, advancing automated narrative generation and interdisciplinary media analysis.

Findings

Demonstrates effective retrieval and montage generation from Italian TV archives

Shows potential for new forms of automated cultural and media analysis

Provides a publicly available dataset for further research

Abstract

This paper introduces AI Blob!, an experimental system designed to explore the potential of semantic cataloging and Large Language Models (LLMs) for the retrieval and recontextualization of archival television footage. Drawing methodological inspiration from Italian television programs such as Blob (RAI Tre, 1989-), AI Blob! integrates automatic speech recognition (ASR), semantic embeddings, and retrieval-augmented generation (RAG) to organize and reinterpret archival content. The system processes a curated dataset of 1,547 Italian television videos by transcribing audio, segmenting it into sentence-level units, and embedding these segments into a vector database for semantic querying. Upon user input of a thematic prompt, the LLM generates a range of linguistically and conceptually related queries, guiding the retrieval and recombination of audiovisual fragments. These fragments are algorithmically selected and structured into narrative sequences producing montages that emulate editorial practices of ironic juxtaposition and thematic coherence. By foregrounding dynamic, content-aware retrieval over static metadata schemas, AI Blob! demonstrates how semantic technologies can facilitate new approaches to archival engagement, enabling novel forms of automated narrative construction and cultural analysis. The project contributes to ongoing debates in media historiography and AI-driven archival research, offering both a conceptual framework and a publicly available dataset to support further interdisciplinary experimentation.