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

arXiv: https://arxiv.org/abs/2210.09716 · HTML: https://mlsanity.com/paper/2210.09716/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [The Journal Coverage of Web of Science, Scopus and Dimensions: A Comparative Analysis](https://mlsanity.com/paper/2011.00223/) (2020)
- [Characterization, description, and considerations for the use of funding acknowledgement data in Web of Science](https://mlsanity.com/paper/1604.04780/) (2016)
- [Making visible the invisible through the analysis of acknowledgements in the humanities](https://mlsanity.com/paper/1706.06334/) (2017)
- [Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents](https://mlsanity.com/paper/2206.10939/) (2022)
- [Normative versus strategic accounts of acknowledgment data: the case of the top-five journals of economics](https://mlsanity.com/paper/2105.12988/) (2021)

## Built on by (most cited first, 1 of 1)

- [Embedding Models for Supervised Automatic Extraction and Classification of Named Entities in Scientific Acknowledgements](https://mlsanity.com/paper/2307.13377/) (2023)

## Abstract (arXiv)

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.