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Mapping the Trending Topics of Bibliometric-enhanced Information Retrieval

This paper aims to map the trending topics of Bibliometric-enhanced Information Retrieval (BIR) based on co-word network analysis and strategic diagram. The bibliographic dataset was gathered from proceedings and special issues from 2014 to 2021 which yields a final corpus of 227 papers. The unit of analysis was the titles, abstracts, and keywords. For the co-word network, we found five communities: Scientific Summarization Shared Tasks (SSST), Information Retrieval (IR), Bibliometrics and Science Mapping (BSM), Citation Context Analysis (CCA), and Mathematical Citation Context Analysis (MCCA). The first and the fourth communities are highly based on NLP and Computational Linguistics. The strategic map identified SST, CCA, and IR as BIR’s core themes. Furthermore, language models were recognized as a hot topic that is mostly used in scientific summarization. In the beginning, BIR’s focus was on bibliometrics and informational retrieval, but in recent years with the developments in NLP, other topics such as scientific-fact checking, argumentation mining, information extraction, scientific search based on Question and Answering are being studied.