A Content-Based Approach to Social Network Analysis: A Case Study on Research Communities

  • Dario De Nart
  • Dante Degl’Innocenti
  • Marco Basaldella
  • Maristella Agosti
  • Carlo Tasso
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 612)

Abstract

Several works in literature investigated the activities of research communities using big data analysis, but the large majority of them focuses on papers and co-authorship relations, ignoring that most of the scientific literature available is already clustered into journals and conferences with a well defined domain of interest. We are interested in bringing out underlying implicit relationships among such containers and more specifically we are focusing on conferences and workshop proceedings available in open access and we exploit a semantic/conceptual analysis of the full free text content of each paper. We claim that such content-based analysis may lead us to a better understanding of the research communities’ activities and their emerging trends. In this work we present a novel method for research communities activity analysis, based on the combination of the results of a Social Network Analysis phase and a Content-Based one. The major innovative contribution of this work is the usage of knowledge-based techniques to meaningfully extract from each of the considered papers the main topics discussed by its authors.

Keywords

Content-based Social network analysis Social semantic Research communities Text processing Clustering Scientific publishing 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dario De Nart
    • 1
  • Dante Degl’Innocenti
    • 1
  • Marco Basaldella
    • 1
  • Maristella Agosti
    • 2
  • Carlo Tasso
    • 1
  1. 1.Artificial Intelligence Lab, Department of Mathematics and Computer ScienceUniversity of UdineUdineItaly
  2. 2.University of PaduaPaduaItaly

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