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The Age of Confidentiality: A Review of the Security in Social Networks and Internet

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 290)

Abstract

Security based on content analysis in social networks has become a hot spot as a result of the recent problems of violations of privacy by governments to international security agencies. This article is an approach to the implementation of programs for extraction and analysis of the web information, this includes the technical difficulties and the legal consequences involved.

Keywords

Social networks analysis security data meaning information fusion wrappers protection data laws organizations agents 

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References

  1. 1.
    W3C Semantic Web Activity. World Wide Web Consortium (W3C) (November 7, 2011) (retrieved November 26, 2011)Google Scholar
  2. 2.
    Kushmerick, N., Weld, D.S., Doorenbos, R.: Wrapper Induction for Information Extraction. In: Proceedings of the International Joint Conference on Artificial Intelligence (1997)Google Scholar
  3. 3.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer (2007)Google Scholar
  4. 4.
    Dalvi, N., Kumar, R., Soliman, M.: Automatic wrappers for large scale web extraction. Proceedings of the VLDB Endowment 4(4), 219–230 (2011)Google Scholar
  5. 5.
    Kushmerick, N., Weld, D.S., Doorenbos, R.: Wrapper Induction for Information Extraction. In: Proceedings of the International Joint Conference on Artificial Intelligence (1997)Google Scholar
  6. 6.
    Laender, A., Ribeiro-Neto, B., Silva, A., Teixeira, J.: A Brief Survey of Web Data Extraction Tools. SIGMOD Record 31(2) (June 2002)Google Scholar
  7. 7.
    Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media (2009) ISBN 978-0-596-51649-9Google Scholar
  8. 8.
    Alderberg, B.: NoDoSe – A Tool for semi-automatically extracting structured and semistructured data from text-documents. In: Proceddings of ACM SIGMON International Conference on Management of Data, SeattleGoogle Scholar
  9. 9.
    Glimm, B., Horrocks, I., Motik, B., Shearer, R., Stoilos, G.: A Novel Approach to Ontology Classification. J. of Web Semantics 14, 84–101 (2012)CrossRefGoogle Scholar
  10. 10.
    Stuckenschmidt, H., Klein, M.: Structure-based partitioning of large concept hierarchies. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 289–303. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Sentencia del Tribunal Supremo de España 9 de Octubre de, número 572/2012 (2012)Google Scholar
  12. 12.
    Anheier, H.K., Gerhards, J., Romo, F.P.: Forms of capital and social structure of fields: Examining Bourdieu’s social topography. American Journal of Sociology 100, 859–903 (1995)CrossRefGoogle Scholar
  13. 13.
    De Nooy, W.: Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory. Poetics 31, 305–327 (2003)CrossRefGoogle Scholar
  14. 14.
    Senekal, B.A.: Die Afrikaanse literêre sisteem: ’n Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA), LitNet Akademies (2012)Google Scholar
  15. 15.
    Burt, S.R.: Structural Holes: The Social Structure of Competition. Havard University Press (1992)Google Scholar
  16. 16.
    Coskun, G., Rothe, M., Teymourian, K., Paschke, A.: Applying Community Detection Algorithms on Ontologies for Identifying Concept Groups. In: Proc. of the Fifth International Workshop on Modular Ontologies (WoMO 2011), pp. 12–24 (2011)Google Scholar
  17. 17.
    Leskovec, J., Lang, K.J., Mahoney, M.W.: Empirical Comparison of Algorithms for Network Community Detection. In: WWW 2010: ACM WWW International Conference on World Wide Web (2010)Google Scholar
  18. 18.
    Guthrie, D.: Unsupervised Detection of Anomalous Text. Department of Computer Science University of Sheeld (July 2008), http://nlp.shef.ac.uk/Completed_PhD_Projects/guthrie.pdf
  19. 19.
    Barrientos, F., Ríos, S.A.: Aplicación de Minería de Datos para Predecir Fuga de Clientes en la Industria de las Telecomunicaciones. Revista de Ingeniería de Sistemas 27 (Septiembre 2013)Google Scholar
  20. 20.
    The Camaleon Web Wrapper Engine, http://chameleon.readthedocs.org/en/latest
  21. 21.
    RDF. Semantic Web Standards, http://www.w3.org/RDF/
  22. 22.
    Ahmed, A., et al.: GEOMI: GEOmetry for Maximum Insight. In: Healy, P., Nikolov, N.S. (eds.) GD 2005. LNCS, vol. 3843, pp. 468–479. Springer, Heidelberg (2006), http://sydney.edu.au/engineering/it/~cmurray/geomi.pdf CrossRefGoogle Scholar
  23. 23.
    Liddle, S.W., Embley, D.W., Scott, D.T., Yau, S.H.: Extracting Data Behind Web Form. In: Olivé, À., Yoshikawa, M., Yu, E.S.K. (eds.) ER 2003. LNCS, vol. 2784, pp. 402–413. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  24. 24.
    Garijo, F., Gómes-Sanz, J.J., Pavón, J., Massonet, P.: Multi-agent system organization: An engineering perspective. In: Pre-Proceeding of the 10th European Workshop on Modeling Autonomous Agents in a Multi-Agent World, MAAMAW 2001 (2001)Google Scholar
  25. 25.
    Castellanos-Garzón, J., García, C., Novais, P., Díaz, F.: A visual analytics framework for cluster analysis of DNA microarray data. Expert Systems With Applications 40(2), 758–774 (2013) ISSN: 0957-4174Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Universidad de SalamancaSalamancaSpain
  2. 2.Laboratoire d’Informatique de Grenoble - CNRSGrenobleFrance

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