Visualizing Cultural Digital Resources using Social Network Analysis

Antonio Capodieci, Daniele D’Aprile, Gianluca Elia, Francesca Grippa, Luca Mainetti


This paper describes the design and implementation of a prototype to extract, collect and visually analyse cultural digital resources using social network analysis empowered with semantic features. An initial experiment involved the collection and visualization of connections between cultural digital resources - and their providers - stored in the platform DiCet (an Italian Living Lab centred on Cultural Heritage and Technology). This step helped to identify the most appropriate relational data model to use for the social network visualization phase. We then run a second experiment using a web application designed to extract relevant data from the platform The actors in our two-mode networks are Cultural Heritage Objects (CHOs) shared by institutional and individual providers, such as galleries, museums, individual experts and content aggregators. The links connecting nodes represent the digital resources associated to the CHOs. The application of the prototype offers insights on the most prominent providers, digital resources and cultural objects over time. Through the application of semantic analysis, we were also able to identify the most used words and the related sentiment associated to them.


  1. Beaudoin J.E. (2012) A Framework for Contextual Metadata Used in the Digital Preservation of Cultural Objects. D-Lib Magazine. 11(11/12).
  2. Borgatti, S. P., Everett, M.G. (2006). A graph-theoretic framework for classifying centrality measures. Social Networks 28(4): 466-484.
  3. Boujemaa N, Gouet V, Ferecatua M (2002) Approximate Search vs. Precise Search by Visual Content. In Proceedings of MIR Workshop ACM-MM.
  4. Brönnimann, L. (2014) Multilanguage sentiment analysis of Twitter data on the example of Swiss politicians. M.Sc. Thesis, University of Applied Sciences Northwestern Switzerland; retrieved from sis_Lucas_Broennimann.pdf.
  5. Campbell, R.S., Pennebaker, J.W. (2003). The secret life of pronouns: Flexibility in writing style and physical health. Psychological Science, 14: 60-65.
  6. D'Agata R., Gozzo S., Tomaselli V. (2012) Network analysis approach to map tourism mobility, Quality & Quantity, 47, (6): 3167-3184.
  7. Erétéo G., Buffa M., Gandon F., Grohan P., Leitzelman M., Sander P. (2008). A state of the art on social network analysis and its applications on a semantic web. SDoW2008, workshop at ISWC.
  8. Europeana Creative, D2.1 - Metadata Retrieval Services Based on Semantic Web Technologies, retrieved on 05/26/2015 from Europeana_Professional/Projects/Project_list/Europea na_Creative/Deliverables/eCreative_D2.1_NTUA_v1. 0.pdf
  9. Gawinecki, M. (2008). How schema mapping can help in data integration?-integrating the relational databases with ontologies. ICT School, Computer Science.
  10. Gloor, P. Krauss, J. Nann, S, Fischbach, K. Schoder, D. (2009) Web Science 2.0: Identifying Trends through Semantic Social Network Analysis. IEEE Conference on Social Computing (SocialCom-09), Aug 29-31, Vancouver.
  11. Kwan P.W., Kameyama K., Gao J., Toraichi K. (2011) Content-based image retrieval of cultural heritage symbols by interaction of visual perspectives. International Journal of Pattern Recognition and Artificial Intelligence, 25(05), 643-673.
  12. Miguéns, J., Baggio, R., Costa, C. (2008). Social media and Tourism Destination: TripAdvisor Case Study. Proceedings of the IASK Advances in Tourism Research 2008 (ATR2008), Aveiro, Portugal, 26-28 May 2008, 194-199.
  13. Pang B, Lee L. and Vaithyanathan S. (2002) Thumbs up? sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 79-86.
  14. Pietiläinen A.-K., Oliver E., LeBrun J., Varghese G., and C. Diot. (2009) MobiClique: Middleware for Mobile Social Networking. In WOSN.
  15. Schoder, D., Gloor, P. A., & Metaxas, P. T. (2013). Social Media and Collective Intelligence-Ongoing and Future Research Streams. KI-Künstliche Intelligenz, 27(1), 9-15.
  16. Shadish, W. R., Cook T.D, and Campbell D.T. (2002) Experimental and quasi-experimental designs for generalized causal inference." Social Service Review 76, no. 3, 510-514.
  17. Shi, K., Gao, F., Xu, Q., & Xu, G. (2014). Integration framework with semantic aspect of heterogeneous system based on ontology and ESB. In Control and Decision Conference (2014 CCDC), The 26th Chinese (pp. 4143-4148). IEEE.
  18. Smiciklas, M. (2012). The power of infographics: Using pictures to communicate and connect with your audiences. Que Publishing.
  19. Tonti, S., Baggio, R. (2012). Organizational impacts of social network analysis for an Italian multinational enterprise. Turistica - Italian Journal of Tourism and Culture, 21(1), 65-73.
  20. Tsirliganis N., Pavlidis G., Koutsoudis A., Papadopoulou D., Tsompanopoulos A., Stavroglou K., Loukou Z., Chamzas C., (2004) Archiving Cultural Objects in the 21st Century, Elsevier Journal of Cultural Heritage, Vol. 5, Issue 4, pp. 379-384.
  21. Xia, F., Yang, L.T., Wang, L., Vinel, A. (2012) Internet of Things. International Journal of Communication Systems. 25, 1101-1102.
  22. Wasserman, S. and Faust, K. (1994). Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences, Cambridge University Press, Cambridge.
  23. Wache, H., Voegele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., & Hübner, S. (2001, August). Ontology-based integration of information-a survey of existing approaches. In IJCAI-01 workshop: ontologies and information sharing (Vol. 2001, pp. 108-117).
  24. Whitelaw, C. Garg, N. Argamon, S. (2005) Using Appraisal Groups for Sentiment Analysis, in Proceedings of the 14th ACM international conference on Information and Knowledge Management, p. 631.
  25. Zhang X, Gloor P.A., Grippa F. (2013) Measuring creative performance of teams through dynamic semantic social network analysis. International Journal of Organisational Design and Engineering, 3(2): 165- 184.
  26. Zhu, W. (2012). Semantic mediation bus: an ontologybased runtime infrastructure for service interoperability. In Enterprise Distributed Object Computing Conference Workshops (EDOCW), 2012 IEEE 16th International (pp. 140-145). IEEE.

Paper Citation

in Harvard Style

Capodieci A., D’Aprile D., Elia G., Grippa F. and Mainetti L. (2015). Visualizing Cultural Digital Resources using Social Network Analysis . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 186-194. DOI: 10.5220/0005585801860194

in Bibtex Style

author={Antonio Capodieci and Daniele D’Aprile and Gianluca Elia and Francesca Grippa and Luca Mainetti},
title={Visualizing Cultural Digital Resources using Social Network Analysis},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},

in EndNote Style

JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Visualizing Cultural Digital Resources using Social Network Analysis
SN - 978-989-758-158-8
AU - Capodieci A.
AU - D’Aprile D.
AU - Elia G.
AU - Grippa F.
AU - Mainetti L.
PY - 2015
SP - 186
EP - 194
DO - 10.5220/0005585801860194