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...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 222 |
|---|---|
| container_issue | |
| container_start_page | 215 |
| container_title | |
| container_volume | 4 |
| creator | Gloor, P.A. Krauss, J. Nann, S. Fischbach, K. Schoder, D. |
| 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. |
| doi_str_mv | 10.1109/CSE.2009.186 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5284145</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5284145</ieee_id><sourcerecordid>5284145</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2646-9c6970fe1d820606e8a3435581aa73b1dd565742605bee350c9707667684cdc83</originalsourceid><addsrcrecordid>eNotj01Lw0AYhFdEUGtu3rzsH0jc7w9vJVQtBD2k4rFsdt-0q2ki2Yjk3xvQuQzDMwwMQreUFJQSe1_Wm4IRYgtq1Bm6JlpZyQ3j9BxlVhsqmBCScyEuUZbSB1m0ZMHoFareocG1j9B7wKwgD3gboJ9iO8f-gHcj9CHh6TgO34cjruHkFuZxPfjoOvwC088wfuJ177o5xXSDLlrXJcj-fYXeHje78jmvXp-25brKPVNC5dYrq0kLNBhGFFFgHBdcSkOd07yhIUgltWCKyAaAS-KXulZKKyN88Iav0N3fbgSA_dcYT26c95IZQZdfv31kS0I</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Web Science 2.0: Identifying Trends through Semantic Social Network Analysis</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Gloor, P.A. ; Krauss, J. ; Nann, S. ; Fischbach, K. ; Schoder, D.</creator><creatorcontrib>Gloor, P.A. ; Krauss, J. ; Nann, S. ; Fischbach, K. ; Schoder, D.</creatorcontrib><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.</description><identifier>ISBN: 9781424453344</identifier><identifier>ISBN: 1424453348</identifier><identifier>EISBN: 0769538231</identifier><identifier>EISBN: 9780769538235</identifier><identifier>DOI: 10.1109/CSE.2009.186</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Blogs ; Computer networks ; Data mining ; Filtering algorithms ; Information analysis ; semantic social network analysis ; Social network analysis ; Social network services ; Software algorithms ; Software systems ; trend prediction ; Visualization ; Web mining</subject><ispartof>2009 International Conference on Computational Science and Engineering, 2009, Vol.4, p.215-222</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2646-9c6970fe1d820606e8a3435581aa73b1dd565742605bee350c9707667684cdc83</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5284145$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5284145$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Gloor, P.A.</creatorcontrib><creatorcontrib>Krauss, J.</creatorcontrib><creatorcontrib>Nann, S.</creatorcontrib><creatorcontrib>Fischbach, K.</creatorcontrib><creatorcontrib>Schoder, D.</creatorcontrib><title>Web Science 2.0: Identifying Trends through Semantic Social Network Analysis</title><title>2009 International Conference on Computational Science and Engineering</title><addtitle>CSE</addtitle><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.</description><subject>Algorithm design and analysis</subject><subject>Blogs</subject><subject>Computer networks</subject><subject>Data mining</subject><subject>Filtering algorithms</subject><subject>Information analysis</subject><subject>semantic social network analysis</subject><subject>Social network analysis</subject><subject>Social network services</subject><subject>Software algorithms</subject><subject>Software systems</subject><subject>trend prediction</subject><subject>Visualization</subject><subject>Web mining</subject><isbn>9781424453344</isbn><isbn>1424453348</isbn><isbn>0769538231</isbn><isbn>9780769538235</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj01Lw0AYhFdEUGtu3rzsH0jc7w9vJVQtBD2k4rFsdt-0q2ki2Yjk3xvQuQzDMwwMQreUFJQSe1_Wm4IRYgtq1Bm6JlpZyQ3j9BxlVhsqmBCScyEuUZbSB1m0ZMHoFareocG1j9B7wKwgD3gboJ9iO8f-gHcj9CHh6TgO34cjruHkFuZxPfjoOvwC088wfuJ177o5xXSDLlrXJcj-fYXeHje78jmvXp-25brKPVNC5dYrq0kLNBhGFFFgHBdcSkOd07yhIUgltWCKyAaAS-KXulZKKyN88Iav0N3fbgSA_dcYT26c95IZQZdfv31kS0I</recordid><startdate>2009</startdate><enddate>2009</enddate><creator>Gloor, P.A.</creator><creator>Krauss, J.</creator><creator>Nann, S.</creator><creator>Fischbach, K.</creator><creator>Schoder, D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2009</creationdate><title>Web Science 2.0: Identifying Trends through Semantic Social Network Analysis</title><author>Gloor, P.A. ; Krauss, J. ; Nann, S. ; Fischbach, K. ; Schoder, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2646-9c6970fe1d820606e8a3435581aa73b1dd565742605bee350c9707667684cdc83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithm design and analysis</topic><topic>Blogs</topic><topic>Computer networks</topic><topic>Data mining</topic><topic>Filtering algorithms</topic><topic>Information analysis</topic><topic>semantic social network analysis</topic><topic>Social network analysis</topic><topic>Social network services</topic><topic>Software algorithms</topic><topic>Software systems</topic><topic>trend prediction</topic><topic>Visualization</topic><topic>Web mining</topic><toplevel>online_resources</toplevel><creatorcontrib>Gloor, P.A.</creatorcontrib><creatorcontrib>Krauss, J.</creatorcontrib><creatorcontrib>Nann, S.</creatorcontrib><creatorcontrib>Fischbach, K.</creatorcontrib><creatorcontrib>Schoder, D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gloor, P.A.</au><au>Krauss, J.</au><au>Nann, S.</au><au>Fischbach, K.</au><au>Schoder, D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Web Science 2.0: Identifying Trends through Semantic Social Network Analysis</atitle><btitle>2009 International Conference on Computational Science and Engineering</btitle><stitle>CSE</stitle><date>2009</date><risdate>2009</risdate><volume>4</volume><spage>215</spage><epage>222</epage><pages>215-222</pages><isbn>9781424453344</isbn><isbn>1424453348</isbn><eisbn>0769538231</eisbn><eisbn>9780769538235</eisbn><abstract>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.</abstract><pub>IEEE</pub><doi>10.1109/CSE.2009.186</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781424453344 |
| ispartof | 2009 International Conference on Computational Science and Engineering, 2009, Vol.4, p.215-222 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_5284145 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Blogs Computer networks Data mining Filtering algorithms Information analysis semantic social network analysis Social network analysis Social network services Software algorithms Software systems trend prediction Visualization Web mining |
| title | Web Science 2.0: Identifying Trends through Semantic Social Network Analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T21%3A54%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Web%20Science%202.0:%20Identifying%20Trends%20through%20Semantic%20Social%20Network%20Analysis&rft.btitle=2009%20International%20Conference%20on%20Computational%20Science%20and%20Engineering&rft.au=Gloor,%20P.A.&rft.date=2009&rft.volume=4&rft.spage=215&rft.epage=222&rft.pages=215-222&rft.isbn=9781424453344&rft.isbn_list=1424453348&rft_id=info:doi/10.1109/CSE.2009.186&rft_dat=%3Cieee_6IE%3E5284145%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=0769538231&rft.eisbn_list=9780769538235&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5284145&rfr_iscdi=true |