Upcoming talk by Eugenio Petrovic @DS² 2021 conference

We are glad to announce an upcoming talk by Eugenio Petrovich at the DS² 2021 conference (Digital Studies of Digital Science) @UCLouvain. Here more details on the program.

The conference aims to connect two groups of scholars working on separate trends: the digitalizing of science’s products on one side, and the spread of digital methods on the other side (network and citation analysis, textual analysis and other tools of the digital humanities). The conference will take place, virtually, from March 15–18, 2021. Among the speakers, keynote talks will be held by Katy Börner, Sabina Leonelli, and Christophe Malaterre.

Eugenio Petrovich will present his work on Acknowledgments Co-Mention Networks: A new method for mapping the social structure of scholarly fields.

An abstract of his speech is reported below:

Researchers in bibliometrics and scientometrics have developed in the last decades powerful techniques for mapping the intellectual structure of research fields. Methods based on citation analysis (direct citation links, co-citation analysis and bibliographic coupling) and textual analysis (co-word analysis) allow us to produce detailed science maps to visualize and investigate the structure and development of research fronts, knowledge badges, and communication flows between research areas (Petrovich, 2020) . As to the social structure of science, i.e., the network of interacting researchers underlying the development of scientific ideas, however, science mapping offers comparatively a less rich toolbox. Co-authorship networks, which focus on the formal collaboration ties between researchers, have become the standard in this sense (e.g., Newman, 2001 ). However, co-authorship analysis suffers from well-known limitations related to the practices of authorship. One the one hand, the contributions of the co-authors may vary considerably, including the limit case of “gift authorships”, where the authorship is not matched by any real contribution to the work. On the other hand, not all forms of scientific collaboration result in co-authorship (Laudel, 2002). Especially in the humanities and the social sciences, where authorship seems to be strictly dependent on the material writing of the publication (Hellqvist, 2009), informal collaboration may be particularly difficult to track by co-authorship.
In the light of these shortcomings, scholars such as Blaise Cronin have proposed to examine the acknowledgments of scientific publications to gain a richer picture of collaboration practices and to capture informal collaboration ties in particular (Cronin, 1995) . Acknowledgments analysis seems especially valuable for fields in the social sciences and the humanities, where co-authorship is scarcely diffused and, hence, co-authorship networks are too sparse for being truly informative (Díaz-Faes & Bordons, 2017) .
In this study, we draw on the bibliometric literature on acknowledgments analysis to propose a new method to map the social structure of science. This method relies on the analysis of co-mention of researchers in publications’ acknowledgments. Specifically, the acknowledgmee co-mention network is constructed using the scholars mentioned in the acknowledgments (the so-called “acknowledgees”) as nodes and connecting them when they are mentioned together in the same acknowledgments. The strength of the link is then set proportional to the number of acknowledgments in which scholars are mentioned together. Lastly, clustering algorithm are applied to the network to reveal communities of frequently co-mentioned acknowledgees, obtaining a map of the informal groups of collaborating researchers, akin to invisible colleges (Crane, 1972) , that constitute the social texture of research fields.
To assess the viability and interest of this method, we tested it on a humanities field, i.e., analytic philosophy, that was recently already investigated with citation analysis techniques (Petrovich & Buonomo, 2018) . We manually collected the acknowledgment sections of 2073 articles published in five prestigious analytic philosophy journals in the timespan 2005-2019. Then, we extracted the names of the acknowledgees with an algorithm for named entity recognition and manually consolidated the data by merging names’ variants and correcting errors. Lastly, we constructed the acknowledgee co-mention network and studied both its structural properties and dynamics over time.
Results show that the community of acknowledgees is four times bigger than the community of formal authors, that few acknowledgees are highly mentioned whereas most of them receive only one mention, and that the clusters of co-mention acknowledgees partially reflect the intellectual structure of the field. Interestingly, the acknowledgee co-mention network is denser and less divided in clusters than the co-citation network extracted from the same set of publications, showing that the social structure of analytic philosophy is somehow more inter-connected than its intellectual structure, i.e., that the differentiation in research sub-disciplines is more pronounced at the intellectual than at the social level.
These results show that acknowledgments mapping can be a promising new tool for researchers interested in digital methods for the social analysis of scholarly communities.

Cited references

Crane, D. (1972). Invisible colleges; diffusion of knowledge in scientific communities. University of Chicago Press.
Cronin, B. (1995). The Scholar’s Courtesy: The Role of Acknowledgement in the Primary Communication Process. Taylor Graham.
Díaz-Faes, A. A., & Bordons, M. (2017). Making visible the invisible through the analysis of acknowledgements in the humanities. Aslib Journal of Information Management, 69(5), 576–590. https://doi.org/10.1108/AJIM-01-2017-0008
Hellqvist, B. (2009). Referencing in the humanities and its implications for citation analysis. Journal of the American Society for Information Science and Technology, n/a-n/a. https://doi.org/10.1002/asi.21256
Laudel, G. (2002). What do we measure by co-authorships? Research Evaluation, 11(1), 3–15. https://doi.org/10.3152/147154402781776961
Newman, M. E. (2001). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 64(1), 16131.
Petrovich, E. (2020). Science mapping. ISKO Encyclopedia of Knowledge Organization.
Petrovich, E., & Buonomo, V. (2018). Reconstructing Late Analytic Philosophy. A Quantitative Approach. Philosophical Inquiries, 6(1), 151–181. https://doi.org/10.4454/philinq.v6i1.184

This entry was posted in Data-Driven Research, Digital Humanities, Distant Reading, Quantitative methods. Bookmark the permalink.

Leave a Reply

Your e-mail address will not be published. Required fields are marked *.

This site uses Akismet to reduce spam. Learn how your comment data is processed.