Encouraging interdisciplinary connections at EGU through text mining

An increasing volume of abstracts across geoscience is presented annually at the EGU General Assembly (GA). To manage thousands of abstracts, the conference is structured into divisions, thematic sessions, and individual sessions. However, creating rigid organizational boundaries that separate research contradicts commonly demanded interdisciplinary research: researchers may be only exposed to ideas within their peer group, reinforcing existing perspectives. Such phenomena of filter bubbles and selective exposure to information have been observed in various contexts to limit creativity and innovation. Yet, it persists and remains underexplored in the context of large scientific conferences like the EGU GA. 
In this contribution, we demonstrate how natural language processing allows for breaking the scientific silos to encourage interdisciplinary interaction at EGU GA. We use sentence embeddings (SBERT) to evaluate the semantic similarity between scientific abstracts and identify closely related ones. We analyzed 5,000 randomly selected abstracts per EGU GA, identifying the 10 most similar abstracts. The results show that participants who focus exclusively on abstracts within their thematic session potentially overlook 44% of the ten most relevant contributions to their research, underscoring the risk of missed interdisciplinary connections. Beyond those findings, we will outline existing projects and plans for improving the conference experience and making geoscience research more interdisciplinary.