Finding Scientific Topics in Continuously Growing Text Corpora (2022)

Abstract: 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.

## introduction in the era of "big literature" @xcite , the exponentially growing number of research publications @xcite poses a serious challenge to those trying to keep up with the vast amount of scientific information published every day. on the one hand, this affects scientists and students who want to stay up-do-date. due to the accelerating effects of digitization and globalization (cf. @xcite , assessing scientific developments in a timely manner has become a challenging endeavor -even for experts in their respective fields. a recent example is the plethora of research papers on covid-19 that rapidly grew after the outbreak in 2020 @xcite . the exceptionally large number of researchers @xcite produce scientific output that is arguably too much to be reviewed by individual researchers on a case by case basis. outside academia, on the other hand, journalists, equal contribution. politicians, and the general public are interested in research processes and findings as well. for instance, policy-makers need to evaluate whether a research field is moving toward the intended direction, e.g., whether funding yields scientific output as expected. journalists who want to report the latest trends in research often depend on (potentially biased) expert opinions or conferences that take place only once per year or biennially. this hampers trend detection on a timely, large scale, and reproducible basis. ## methodological background topic modeling is used in many application domains (cf. @xcite , which might be partly due to the intuitive explanation of the model idea: a corpus of documents can be described by distributions of topics over time, where each word in each of these documents is assigned to one of the topics. this in turn yields word distributions for each topic, which are thereby made interpretable. probably the best known model among topic models is the latent dirichlet allocation (lda, blei @xcite . the underlying probabilistic model @xcite is given by for modeling topics in scientific corpora, we use a rolling variant of the classical lda, estimated with the gibbs sampler @xcite , named rollinglda (cf. sect. 2.2). the main challenge is to update the topic model with new publications while preserving the old time series based on the topic assignments of previous models on the one hand and allowing for the creation and mutation of new topics on the other hand. ## framework in order to explore the feasibility of rollinglda for bibliometric purposes, the goals of the current study are threefold we investigate the eligibility of rollinglda for topic identification in scholarly documents by setting different temporal lengths for model initialization as well as different numbers of topics and compare their evolved topics of 2020 to an individual lda model fitted on the 2020 corpus only. we propose a method for time restricted top term weighting that offers additional insights into the evolution of topics. moreover, we illustrate the integration of rollinglda in a topic app. leveraging r shiny @xcite , we present an easy-touse interface to the topic model that, among other things, visualizes topic trends and topic evolution, i.e., the change of topic terms over time. we utilize the approach to the field of psychology as a use case, as psychological research is in most parts empirical, but also comprises theoretical and methodological contributions. this variety in study methodology should favor generalizability of our topic detection approach to other scientific disciplines. ## analysis the five model variants with highest cosine similarity to the reference model (cf. sect. 3.3.2 and 3.3.3) comprise either 200 or 300 topics, while their rollinglda starting years ranged from 1995 to 2015. ## discussion in this paper, we applied rollinglda to a continuously growing corpus of scholarly documents. using the field of psychology as an use case, we found that rollinglda is capable of integrating the annual updates of the database to meaningful topics. the framework can be easily applied to any scientific discipline or even to multiple fields. for this, the text input should at least consist of titles and abstracts. in addition, we recommend controlled keywords (e.g., mesh terms), as they provide the main contents of the articles in a standardized manner. regarding metadata, we used the year of publication, the classification category, and the study methodology (e.g., empirical research, theoretical discussion). this allows to analyze temporal trends, to validate topic contents, and to highlight topics that might be suitable for meta-analyses. however, our approach is not limited to these metadata and many other additions are conceivable. for instance, the share of open access articles or study preregistrations over time could be compared between topics and research fields. the model is implemented as a shiny app that lets users explore and analyze the topics and trends without the need of programming skills, while the open source code facilitates the mentioned modifications to the psy-chtopics app.