A comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domains

Abstract

Analysis of acknowledgments is particularly interesting as acknowledgments may give information not only about funding, but they are also able to reveal hidden contributions to authorship and the researcher’s collaboration patterns, context in which research was conducted, and specific aspects of the academic work. The focus of the present research is the analysis of a large sample of acknowledgement texts indexed in the Web of Science (WoS) Core Collection. Record types “article” and “review” from four different scientific domains, namely social sciences, economics, oceanography and computer science, published from 2014 to 2019 in a scientific journal in English were considered. Six types of acknowledged entities, i.e., funding agency, grant number, individuals, university, corporation and miscellaneous, were extracted from the acknowledgement texts using a named entity recognition tagger and subsequently examined. A general analysis of the acknowledgement texts showed that indexing of funding information in WoS is incomplete. The analysis of the automatically extracted entities revealed differences and distinct patterns in the distribution of acknowledged entities of different types between different scientific domains. A strong association was found between acknowledged entity and scientific domain, and acknowledged entity and entity type. Only negligible correlation was found between the number of citations and the number of acknowledged entities. Generally, the number of words in the acknowledgement texts positively correlates with the number of acknowledged funding organizations, universities, individuals and miscellaneous entities. At the same time, acknowledgement texts with the larger number of sentences have more acknowledged individuals and miscellaneous categories.

Embedding models for supervised automatic extraction and classification of named entities in scientific acknowledgements

“…Training without the MISC category did not show significant performance progress either. Moreover, further analysis of acknowledged entities showed that the miscellaneous category contained very inhomogeneous and partly irrelevant data, making the analysis more complicated (Smirnova & Mayr, 2023). Therefore, we assume that the model would make better predictions if the number of entity types is expanded and miscellaneous categories excluded, i.e., the MISC category could be split into the following categories: names of projects, names of conferences, names of software and dataset.…”

“…Generally, no improvement in increasing the size of the corpus for the FUND category can be explained by the ambiguous nature of the entities which fall into the FUND category and their semantical proximity with other types of entities. Analysis of the extracted entities showed that many entities were extracted correctly, but were assigned to the wrong category (Smirnova & Mayr, 2023). Therefore, an additional classification algorithm applied to extracted entities could improve the model's performance.…”