Finding Scientific Topics in Continuously Growing Text Corpora

The ever growing amount of research publications demands computational assistance for everyone trying to keep track with scientific processes. Topic modeling has become a popular approach for finding scientific topics in static collections of research papers. However, the reality of continuously growing corpora of scholarly documents poses a major challenge for traditional approaches. We introduce RollingLDA for an ongoing monitoring of research topics, which offers the possibility of sequential modeling of dynamically growing corpora with time consistency of time series resulting from the modeled texts. We evaluate its capability to detect research topics and present a Shiny App as an easy-to-use interface. In addition, we illustrate usage scenarios for different user groups such as researchers, students, journalists, or policy-makers. This is the Accepted Manuscript of: Bittermann, A. & Rieger, J. (2022). Finding Scientific Topics in Continuously Growing Text Corpora. In Arman Cohan et al. (Eds.), Proceedings of the Third Workshop on Scholarly Document Processing (pp. 7–18), Gyeongju, Republic of Korea. Association for Computational Linguistics. https://aclanthology.org/2022.sdp-1.2/ reviewed acceptedVersion