Astrobiology is a scientific field that is very interdisciplinary and developing very fast, with many new discoveries generating a high level of attention in both the scientific community and the public. A central goal of astrobiology is to discover life beyond Earth which is, with our current instrumentation and knowledge, arguably within our reach. However, knowledge exchange crossing disciplinary boundaries is becoming increasingly challenging due to different usage of nomenclature and scientific controversies often limited to subdisciplines. There have been some efforts to compile organized databases of terms, concepts and other relevant material within some of the subfields contributing to astrobiology, for example through manually curated online portals designed to benefit students, teachers and practitioners of astrobiology-related research. However, the developments within the subfields and the potentially premature communication of research findings are too fast for objective research portals to remain reliable and up-to-date enough to enable well-informed scientific discussions. We suggest here a novel strategy for developing an online tracers portal as a self-maintaining and self-updating information platform, that would allow not only for a relatively unbiased selection of research results, but also provide fast access to latest scientific discoveries together with potential controversies, such that users of the tracers portal can form their own opinion on all available data rather than obtaining an already filtered and potentially biased selection of information.

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.

Social network analysis is known to provide a wealth of insights relevant to many aspects of policymaking. Yet, the social data needed to construct social networks are not always available. Furthermore, even when they are, interpreting such networks often relies on extraneous knowledge. Here, we propose an approach to infer social networks directly from the texts produced by actors and the terminological similarities that these texts exhibit. This approach relies on fitting a topic model to the texts produced by these actors and measuring topic profile correlations between actors. This reveals what can be called “hidden communities of interest,” that is, groups of actors sharing similar semantic contents but whose social relationships with one another may be unknown or underlying. Network interpretation follows from the topic model. Diachronic perspectives can also be built by modeling the networks over different time periods and mapping genealogical relationships between communities. As a case study, the approach is deployed over a working corpus of academic articles (domain of philosophy of science; N=16,917).

The central role of such epistemic concepts as theory, explanation, model, or mechanism is rarely questioned in philosophy of science. Yet, what is their actual use in the practice of science? Here, we deploy text-mining methods to investigate the usage of 61 epistemic notions in a corpus of full-text articles from the biological and biomedical sciences (N = 73,771). The influence of disciplinary context is also examined by splitting the corpus into subdisciplinary clusters. The results reveal the intricate semantic networks that these concepts actually form in the scientific discourse, not always following our intuitions, at least in some parts of science.