2025/01/01 by Christophe Malaterre, Francis Lareau · 1 voice
Environmental Science · #Isotope Analysis in Ecology
paper · pdf · doi:10.1017/s1473550424000211
openalex publication_date 2025/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Abstract Astrobiology is often defined as the study of the origin, evolution, distribution and future of life on Earth and in the Universe and thought of as a discipline. In practice though, the delineation of astrobiology-related research and corresponding groups of researchers is far from straightforward. Here, we propose to apply text-mining methods to identify researcher communities depending on thematic similarities in their published works. After fitting a latent Dirichlet allocation topic model to the complete article corpus of three flagship journals in the field – Origins of Life and Evolution of Biospheres (1968–2020), Astrobiology (2001–2020), the International Journal of Astrobiology (2002–2020) – and computing author topic profiles, researcher communities are inferred from topic similarity networks to which community detection is applied. Such semantic social networks reveal, as we call them, ‘hidden communities of interest’ that gather researchers who publish on similar topics. The evolution of these communities is also mapped through time, bringing to light the significant shifts that the discipline underwent in the past 50 years.