Development of a user-friendly app for exploring and analyzing research topics in psychology

This is the code for the PsychTopics app for exploring and analyzing research topics in psychology. Try the demo here: https://abitter.shinyapps.io/psychtopics/

Poster presentation at ISSI 2019 – 17th International Conference on Scientometrics & Informetrics, September 2–5, Rome, Italy

Current approaches for researcher identification on Twitter prove to be effective, but rely on external data sources. This dependency can be a challenge to their sustainability. Here, we report a chain-referral sampling algorithm that uses solely data from the Twitter API. Researchers are identified by crawling the mentions network of a seed sample of verified researchers. We address the two research questions of validity (RQ1) and representativity (RQ2) of the Twitter accounts identified by the algorithm. To answer the first research question, a precision-recall analysis was performed, while to answer the second research question, the distribution of gender, location, and subdiscipline criteria on Twitter was compared to that of publishing authors using the Chi-square test and Fisher's exact test. The results suggest our approach as a solid alternative for the case of missing external data sources. Moreover, our study provides further evidence that Twitter-active researchers should not be regarded as representative of the whole research community.

Latent topics and trends in psychological publications were examined to identify hotspots in psychology. Topic modeling was contrasted with a classification-based scientometric approach in order to demonstrate the benefits of the former. Specifically, the psychological publication output in the German-speaking countries containing German- and English-language publications from 1980 to 2016 documented in the PSYNDEX database was analyzed. Topic modeling based on latent Dirichlet allocation was applied to a corpus of 314,573 publications. Input for topic modeling was the controlled terms of the publications, that is, a standardized vocabulary of keywords in psychology. Based on these controlled terms, 500 topics were determined and trending topics were identified. Hot topics, indicated by the highest increasing trends in this data, were fascets of neuropsychology, online therapy, cross-cultural aspects, traumatization, and visual attention. In conclusion, the findings indicate that topics can reveal more detailed insights into research trends than standardized classifications. Possible applications of this method, limitations, and implications for research synthesis are discussed.