Web Science 2.0: Identifying Trends through Semantic Social Network Analysis
| Autor: | Jonas Krauss, Kai Fischbach, Peter A. Gloor, Stefan Nann, Detlef Schoder |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Social network
business.industry Computer science Sentiment analysis Network science Social web Data science Social Semantic Web Visualization Data visualization Web mining Web science The Internet Semantic social network business Social network visualization Centrality Social network analysis Data Web |
| Zdroj: | CSE (4) |
| DOI: | 10.1109/cse.2009.186 |
| Popis: | 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 amounts 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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