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194 9 Conclusionsresearches have demonstrated that people were willing to provide fairly personal informationabout themselves, which was completely irrelevant to the task at hand, ifprovided the right stimulus to do so. Furthermore, explicitly bringing attention informationprivacy policies had practically no effect. One explanation of this seeminglycontradictory attitude towards privacy may be that we have a bi-modal view of privacy,namely that “I would be willing to share information about myself as long as Iget some (tangible or intangible) benefits from it, as long as there is an implicit guaranteethat the information will not be abused”. The research issue generated by thisattitude is the need to develop approaches, methodologies and tools that can be usedto verify and validate that a Web service is indeed using an end-user’s information ina manner consistent with its stated policies.Temporal evolution of the WebSociety’s interaction with the Web is changing the Web as well as the way the societyinteracts. While storing the history of all of this interaction in one place is clearlytoo staggering a task, at least the changes to the Web are being recorded by thepioneering Internet Archive project. Research needs to be carried out in extractingtemporal models of how Web content, Web structures, Web communities, authorities,hubs, etc. are evolving. Large organizations generally archive (at least portions of)usage data from there Web sites. With these sources of data available, there is a largescope of research to develop techniques for analyzing of how the Web evolves overtime. In another aspect, the analysis of Web structure evolution reveal the insight ofsocial networking changes in the social network analysis.Combining natural language processing and machine learning for really large anddynamic Web dataThe inherent heterogeneity and complex characteristics of Web data nature makesit not an easy job for employing simply traditional data mining algorithms in Webmining tasks. Natural language processing techniques provide additional assistancein such kinds of tasks, which leverages the simulated human intelligent capabilitiesto understand the observed Web data. It is believed the strengths of natural languageprocessing substantially reinforce the Web mining approaches. In real world applications,the analysis scalability and affordability problems of really large and dynamicWeb data largely impair the creditability of Web data mining. To overcomesuch scenarios, practical machine learning approaches is indeed the necessaries. Undoubtedly,the integration of data mining, machine learning and natural languageprocessing techniques would significantly deepen and broaden the Web mining andsocial network analysis landscapes and potentials.The exploration of research experience and techniques obtained from various disciplines,such as database, information retrieval, Web community and data mining,social network analysis, social behavior computing, is needed to discover the underlyingrelationships among Web communities or Web users for social media miningand social networking computing.
References1. http://dms.irb.hr/.2. http://en.wikipedia.org/wiki/.3. http://www.ibm.com/developerworks/linux/library/l-neural/?open&l=805,t=grl,p=NeuralNets.4. M. a. Carreira-perpinan and R. S. Zemel. Proximity graphs for clustering and manifoldlearning. In Advances in Neural Information Processing Systems 17, pages 225–232.MIT Press, 2005.5. C. C. Aggarwal, F. Al-Garawi, and P. Yu. Intelligent crawling on the world wide webwith arbitrary predicates. In the 10th Intl. World Wide Web Conf. (WWW01), pages96–105, 2001.6. R. Agrawal, T. Imieliński, and A. Swami. Mining association rules between sets ofitems in large databases. In SIGMOD ’93: Proceedings of the 1993 ACM SIGMODinternational conference on Management of data, pages 207–216, New York, NY, USA,1993. ACM.7. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery ofassociation rules. Advances in knowledge discovery and data mining, pages 307–328,1996.8. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in largedatabases. In Proceedings 20th International Conference on Very Large Data Bases,pages 487–499, 1994.9. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in largedatabases. In Proceedings of the 20th International Conference on Very Large DataBases (VLDB), pages 487–499, Santiago, Chile, 1994. Morgan Kaufmann.10. R. Agrawal and R. Srikant. Mining sequential patterns. In P. S. Y. Chen and A. S.P., editors, Proceedings of the International Conference on Data Engineering (ICDE),pages 3–14, Taipei, Taiwan, 1995. IEEE Computer Society Press.11. R. Agrawal and R. Srikant. Mining sequential patterns. In Proceedings of InternationalConference on Data Engineering, pages 3–14, 1995.12. R. Albert, H. Jeong, and A. Barabasi. The diameter of the world wide web. Nature,401:130, 1999.13. S. Asur, S. Parthasarathy, and D. Ucar. An event-based framework for characterizingthe evolutionary behavior of interaction graphs. In KDD ’07: Proceedings of the13th ACM SIGKDD international conference on Knowledge discovery and data mining,pages 913–921, New York, NY, USA, 2007. ACM.G. Xu et al., Web Mining and Social Networking,DOI 10.1007/978-1-4419-7735-9, © Springer Science+Business Media, LLC 2011
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Web Mining and Social Networking
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Guandong Xu • Yanchun Zhang • L
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VIIIPrefacefollowing characteristic
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Acknowledgements: We would like to
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XIVContents3.1.2 Basic Algorithms f
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Part IFoundation
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4Web Content MiningIn recent years
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