Web Science 2.0: Identifying Trends through Semantic Social Network Analysis
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 e...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/59353 |
| _version_ | 1826189182382374912 |
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| author | Gloor, Peter A. Krauss, Jonas Nann, Stefan Fischbach, Kai Schoder, Detlef |
| author2 | Massachusetts Institute of Technology. Center for Collective Intelligence |
| author_facet | Massachusetts Institute of Technology. Center for Collective Intelligence Gloor, Peter A. Krauss, Jonas Nann, Stefan Fischbach, Kai Schoder, Detlef |
| author_sort | Gloor, Peter A. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:10:50Z |
| format | Article |
| id | mit-1721.1/59353 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:10:50Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/593532022-09-30T08:06:52Z Web Science 2.0: Identifying Trends through Semantic Social Network Analysis Gloor, Peter A. Krauss, Jonas Nann, Stefan Fischbach, Kai Schoder, Detlef Massachusetts Institute of Technology. Center for Collective Intelligence Gloor, Peter A. Gloor, Peter A. Web mining Social network analysis semantic social network analysis trend prediction 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. 2010-10-14T21:10:00Z 2010-10-14T21:10:00Z 2009-10 2009-08 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-5334-4 978-0-7695-3823-5 INSPEC Accession Number: 10908417 http://hdl.handle.net/1721.1/59353 Gloor, P.A. et al. “Web Science 2.0: Identifying Trends through Semantic Social Network Analysis.” Computational Science and Engineering, 2009. CSE '09. International Conference on. 2009. 215-222. ©2009 Institute of Electrical and Electronics Engineers. en_US http://dx.doi.org/10.1109/CSE.2009.186 International Conference on Computational Science and Engineering, 2009. CSE '09 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Web mining Social network analysis semantic social network analysis trend prediction Gloor, Peter A. Krauss, Jonas Nann, Stefan Fischbach, Kai Schoder, Detlef Web Science 2.0: Identifying Trends through Semantic Social Network Analysis |
| title | Web Science 2.0: Identifying Trends through Semantic Social Network Analysis |
| title_full | Web Science 2.0: Identifying Trends through Semantic Social Network Analysis |
| title_fullStr | Web Science 2.0: Identifying Trends through Semantic Social Network Analysis |
| title_full_unstemmed | Web Science 2.0: Identifying Trends through Semantic Social Network Analysis |
| title_short | Web Science 2.0: Identifying Trends through Semantic Social Network Analysis |
| title_sort | web science 2 0 identifying trends through semantic social network analysis |
| topic | Web mining Social network analysis semantic social network analysis trend prediction |
| url | http://hdl.handle.net/1721.1/59353 |
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