Predicting the future success of scientific publications through social network and semantic analysis.

Autor/inn/en	Fronzetti Colladon, Andrea; D’Angelo, Ciriaco Andrea; Gloor, Peter A.
Titel	Predicting the future success of scientific publications through social network and semantic analysis.
Quelle	In: Scientometrics, (2020) 1, S.357-377 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-020-03479-5
Schlagwörter	Social network analysis; Text mining; Social capital; Abstract; Citability; Scholarly impact
Abstract	Abstract Citations acknowledge the impact a scientific publication has on subsequent work. At the same time, deciding how and when to cite a paper, is also heavily influenced by social factors. In this work, we conduct an empirical analysis based on a dataset of 2010–2012 global publications in chemical engineering. We use social network analysis and text mining to measure publication attributes and understand which variables can better help predicting their future success. Controlling for intrinsic quality of a publication and for the number of authors in the byline, we are able to predict scholarly impact of a paper in terms of citations received 6 years after publication with almost 80% accuracy. Results suggest that, all other things being equal, it is better to co-publish with rotating co-authors and write the papers’ abstract using more positive words, and a more complex, thus more informative, language. Publications that result from the collaboration of different social groups also attract more citations.
Erfasst von	OLC
Update	2023/2/05