Abstract
This chapter reviews existing data mining tools for scraping data from heterogeneous online social networks. It introduces not only the complexities of scraping data from these sources (which include diverse data forms), but also presents currently available tools including their strengths and weaknesses. The chapter introduces our solution to effectively mining online social networks through the development of VoyeurServer, a tool we designed which builds upon the open-source Web-Harvest framework. We have shared details of how VoyeurServer was developed and how it works so that data mining developers can develop their own customized data mining solutions built upon the Web-Harvest framework. We conclude the chapter with future directions of our data mining project so that developers can incorporate relevant features into their data mining applications.
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Murthy, D., Gross, A., Takata, A., Bond, S. (2013). Evaluation and Development of Data Mining Tools for Social Network Analysis. In: Özyer, T., Erdem, Z., Rokne, J., Khoury, S. (eds) Mining Social Networks and Security Informatics. Lecture Notes in Social Networks. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6359-3_10
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DOI: https://doi.org/10.1007/978-94-007-6359-3_10
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