Conference Paper

Web Science 2.0: Identifying Trends through Semantic Social Network Analysis

DOI: 10.2139/ssrn.1299869 Conference: IEEE Conference on Social Computing (SocialCom-09)
Source: DBLP


We introduce a novel set of social network analysis based algorithms for mining the Web, blogs, and online forums to identify trends and find the people launching these new trends. These algorithms have been implemented in Condor, a software system for predictive search and analysis of the Web and especially social networks. Algorithms include the temporal computation of network centrality measures, the visualization of social networks as Cybermaps, a semantic process of mining and analyzing large mounts of text based on social network analysis, and sentiment analysis and information filtering methods. The temporal calculation of betweenness of concepts permits to extract and predict long-term trends on the popularity of relevant concepts such as brands, movies, and politicians. We illustrate our approach by qualitatively comparing Web buzz and our Web betweenness for the 2008 US presidential elections, as well as correlating the Web buzz index with share prices.

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    • "Borgatti and Li (2009) discuss the potential of SNA for supply chain management by applying network concepts to both hard (e.g., material and money flows) and soft (friendships and sharing-of-information) types of ties. Gloor et al. (2009) introduce a novel set of SNA based algorithms for mining the Web, blogs, and online forums to identify trends and find the people launching these new trends. Lin et al. (2008) develop a social networking application, SmallBlue, that unlocks the valuable business intelligence of " who knows what? "
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    • "If a new service or web application is introduced it is referred to as a Web 2.0 service if it owns a subset of the following features: Usercentered Design, Rich Internet Application (RIA), Dynamic Content (DC), Collaboration/Cooperation (CC), Software as a Service (SAAS), Decentralisation of Management/Power/Administration, Crowdsourcing , Web and Rich User Experience. Of course, this development in the WWW towards Web 2.0 applications itself created new large collections of structured data, semi-structured data or nonstructured data and stimulated many knowledge discovery and data mining research projects to search these new data collections for hidden relationships and patterns (Fayyad et al., 1996; Cooley et al., 1997; Nasraoui et al., 2008; Gloor et al., 2009; Munibalaji and Balamurugan, 2012). But since scientists were massive users of the WWW from its beginning at CERN, this was not the only reaction of science to the web development in general and to the Web 2.0 development in particular. "
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    • "The third analysis measures the perception of the C3N project in the external world, tracking if the work of the project team changes perception of Crohn's disease on the Web and in Blogs. Towards that goal, we are using the Web Coolhunting methodology (Gloor et. al. 2009) by identifying the most important Web sites and blogs mentioning the terms we want to track, and then constructing their link network, and measuring the changes in betweenness of the terms. Figure 16 displays the Web and Blog site network of the search terms " lybba " , " improvecarenow " , " ccfa " (Crohn's and Colitis Foundation of A"
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