A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domains

• URL: http://arxiv.org/abs/2210.09716v1
• Date: Tue, 18 Oct 2022 09:50:47 GMT
• Title: A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domains
• Authors: Nina Smirnova and Philipp Mayr
• Abstract summary: 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 were considered. A general analysis of the acknowledgement texts showed that indexing of funding information in WoS is incomplete.
• Score: 5.330844352905488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (NER) 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.

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