Abstract
Discovering and modelling research communities’ activities is a task that can lead to a more effective scientific process and support the development of new technologies. Journals and conferences already offer an implicit clusterization of researchers and research topics, and social analysis techniques based on co-authorship relations can highlight hidden relationships among researchers, however, little work has been done on the actual content of publications.We claim that a content-based analysis on the full text of accepted papers may lead to a better modeling and understanding of communities’ activities and their emerging trends. In this work we present an extensive case study of research community modelling based upon the analysis of over 450 events and 7000 papers.
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CITATION STYLE
De Nart, D., Degl’Innocenti, D., Pavan, A., Basaldella, M., & Tasso, C. (2015). Modelling the user modelling community (And other communities as well). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9146, pp. 357–363). Springer Verlag. https://doi.org/10.1007/978-3-319-20267-9_31
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