Abstract

Our motivation for conducting this research is driven by the lack of studies focusing on the acknowledgments sections of published papers. Another motivation is the lack of a study examining the countries and organizations mentioned in the acknowledgments section and their influence—something that cannot be analyzed using a citation or co-authorship relationship. Concentrating on the qualitative aspects of acknowledgments has been limited because of the atypical pattern of the acknowledgment section. Our research aims to identify useful information hidden within the acknowledgment sections of the articles stored in the PubMed Central database and to analyze a map of influence via a country-acknowledgment network. To solve the problems, we use the topic modeling to analyze topics of acknowledgments and conduct a basic network analysis to find the difference in the co-the country network and acknowledgment network. A word-embedding model is used to compare the semantic similarity that exists between the authors and countries extracted from our original dataset. The result of topic modeling suggests that funding has become a critical topic in acknowledgments. The results of network analysis indicate that some large countries work as hubs in terms of both implicitly and explicitly while revealing that some countries such as China do not frequently work with other countries. The word-embedding model built by acknowledgments suggests that the authors frequently referenced in acknowledgments are also likely to be referred to in a similar context. It also implies that the publishing country of a paper has little effect on whether it receives an acknowledgment from any other specific country. Through these results, we conclude that the content in acknowledgments extracted from the papers can be divided into two categories—funding and appreciation. We also find that there is no clear relationship between the publication country and the countries mentioned in the acknowledgment section.

Keywords:text mining;scientometrics;content-based citation Analysis;full-text analysis

DOI:https://doi.org/10.1515/dim-2017-0003

received May 15, 2017; accepted May 28, 2017.