Periodic Reporting for period 1 - DigEco (Digital Ecocriticism: Moving beyond anthropocentrism in the contemporary French novel)
Reporting period: 2024-01-01 to 2025-12-31
As environmental challenges intensify, literature plays a crucial role in shaping public awareness and ecological consciousness. The DigEco project explores how contemporary French and Francophone fiction ( 800 Novels between 2001–2021) reflects environmental themes, focusing on biodiversity, geographical space, and nonhuman agency. By integrating Digital Humanities and Ecocriticism, DigEco applies AI-driven text analysis to uncover how literature engages with environmental discourse.
The project aligns with EU policy priorities, including the European Green Deal and the EU Biodiversity Strategy 2030, by examining how fiction contributes to climate awareness and sustainability narratives. DigEco’s objectives are:
Tracking Biodiversity in Fiction – Identifying ecological terms and their evolution in literary texts.
Mapping Geographical Representations – Analyzing shifts in the depiction of natural and urban spaces.
Investigating Nonhuman Agency – Assessing how animals and plants are represented as narrative agents.
By leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP), DigEco provides new insights into literature’s evolving role in environmental discourse, benefiting researchers, educators, policymakers, and cultural institutions.
The project aligns with EU policy priorities, including the European Green Deal and the EU Biodiversity Strategy 2030, by examining how fiction contributes to climate awareness and sustainability narratives. DigEco’s objectives are:
Tracking Biodiversity in Fiction – Identifying ecological terms and their evolution in literary texts.
Mapping Geographical Representations – Analyzing shifts in the depiction of natural and urban spaces.
Investigating Nonhuman Agency – Assessing how animals and plants are represented as narrative agents.
By leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP), DigEco provides new insights into literature’s evolving role in environmental discourse, benefiting researchers, educators, policymakers, and cultural institutions.
The project applied computational methods to analyze 800 contemporary novels, focusing on three key areas:
✅ Biodiversity Analysis: Developed a lexicographical database based on the Loterre biodiversity lexicon (CNRS), identifying 746 biodiversity-related terms and tracking their usage trends over two decades.
✅ Geospatial Mapping: Implemented a Named Entity Recognition (NER) tool to extract and analyze place names, analyzing literary focus on ecological landscapes.
✅ Nonhuman Agency Study: Used AI models (GPT-4 API and FlauBERT) to study literal representations of animals and plants distinguish figurative vs. literal representations of animals and plants, analyzing a shift toward decentering human perspectives in fiction.
Key Achievements:
Innovative Research Methodology: First large-scale computational ecocritical study of contemporary French fiction.
New Digital Tools: AI-driven text mining and mapping techniques tailored for ecological literary analysis.
Empirical Evidence: Provided quantitative insights into how literature engages with biodiversity and nonhuman perspectives.
Open Science Contribution: Published datasets and methodologies in open-access repositories, supporting future research.
✅ Biodiversity Analysis: Developed a lexicographical database based on the Loterre biodiversity lexicon (CNRS), identifying 746 biodiversity-related terms and tracking their usage trends over two decades.
✅ Geospatial Mapping: Implemented a Named Entity Recognition (NER) tool to extract and analyze place names, analyzing literary focus on ecological landscapes.
✅ Nonhuman Agency Study: Used AI models (GPT-4 API and FlauBERT) to study literal representations of animals and plants distinguish figurative vs. literal representations of animals and plants, analyzing a shift toward decentering human perspectives in fiction.
Key Achievements:
Innovative Research Methodology: First large-scale computational ecocritical study of contemporary French fiction.
New Digital Tools: AI-driven text mining and mapping techniques tailored for ecological literary analysis.
Empirical Evidence: Provided quantitative insights into how literature engages with biodiversity and nonhuman perspectives.
Open Science Contribution: Published datasets and methodologies in open-access repositories, supporting future research.
DigEco advances Digital Ecocriticism by pioneering AI-driven computational methods to analyze literature. It moves beyond traditional qualitative literary analysis, offering quantitative, large-scale insights.
First AI-Assisted Ecocritical Study – Applied Natural Language Processing (NLP) and Named Entity Recognition (NER) to explore ecological themes across a vast literary corpus in French.
New Biodiversity Lexicon for Literature – Developed a taxonomy of 746 ecological terms, providing measurable data on literature’s engagement with biodiversity.
Geospatial Literary Analysis – Created an automated place-name extraction tool, revealing trends in the portrayal of natural spaces.
AI-Based Study of Nonhuman Agency – Used syntactic and semantic analysis to analyze literary focus on nonhuman narratives.
Future Directions
Expansion of methodology to multilingual literary corpora. Integration with biodiversity databases to enhance interdisciplinary collaboration. Development of AI-powered tools for figurative language detection in fiction. Contribution to digital education by incorporating findings into environmental humanities curricula.
First AI-Assisted Ecocritical Study – Applied Natural Language Processing (NLP) and Named Entity Recognition (NER) to explore ecological themes across a vast literary corpus in French.
New Biodiversity Lexicon for Literature – Developed a taxonomy of 746 ecological terms, providing measurable data on literature’s engagement with biodiversity.
Geospatial Literary Analysis – Created an automated place-name extraction tool, revealing trends in the portrayal of natural spaces.
AI-Based Study of Nonhuman Agency – Used syntactic and semantic analysis to analyze literary focus on nonhuman narratives.
Future Directions
Expansion of methodology to multilingual literary corpora. Integration with biodiversity databases to enhance interdisciplinary collaboration. Development of AI-powered tools for figurative language detection in fiction. Contribution to digital education by incorporating findings into environmental humanities curricula.