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To address the current crises (climatic, social, economic), the self-sufficiency – a set of practices that combine energy sobriety, self-production of food and energy, and self-construction - arouses an increasing interest. The CNRS STAY project (Savoirs Techniques pour l’Auto-suffisance sur YouTube) explores this topic by analyzing techniques shared on YouTube. We present Agro-STAY, a platform designed for the collection, processing, querying and visualization of data from YouTube videos and their comments. We propose a full methodology dedicated to processing YouTube videos, and apply Natural Language Processing (NLP) techniques and language models, which enable a fine-grained analysis of alternative agricultural practices described online. In addition, we provide a well-adapted graphical user interface to help the experts analyzing the extracted knowledge.

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Agro-STAY: Data Collection and Analysis of Alternative Agriculture Practices from YouTube

  • 会议论文

  • 发表日期：

    2026年

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  • Laura Maxim,
  • Julien Rabatel,
  • Jean-Marc Douguet,
  • Natalia Grabar,
  • Roberto Interdonato,
  • Sébastien Loustau,
  • Landy Rajaonarivo,
  • Mathieu Roche,
  • Maguelonne Teisseire

摘要

To address the current crises (climatic, social, economic), the self-sufficiency – a set of practices that combine energy sobriety, self-production of food and energy, and self-construction - arouses an increasing interest. The CNRS STAY project (Savoirs Techniques pour l’Auto-suffisance sur YouTube) explores this topic by analyzing techniques shared on YouTube. We present Agro-STAY, a platform designed for the collection, processing, querying and visualization of data from YouTube videos and their comments. We propose a full methodology dedicated to processing YouTube videos, and apply Natural Language Processing (NLP) techniques and language models, which enable a fine-grained analysis of alternative agricultural practices described online. In addition, we provide a well-adapted graphical user interface to help the experts analyzing the extracted knowledge.