Content Mining of Microblogs
Emergence of Web 2.0, internet users can share their contents with other users using social networks. In this paper microbloggers' contents are evaluated with respect to how they reflect their categories. Migrobloggers' category information, which is one of the four categories that are eco...
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 | 838 |
|---|---|
| container_issue | |
| container_start_page | 835 |
| container_title | |
| container_volume | |
| creator | Cingiz, M. O. Diri, B. |
| description | Emergence of Web 2.0, internet users can share their contents with other users using social networks. In this paper microbloggers' contents are evaluated with respect to how they reflect their categories. Migrobloggers' category information, which is one of the four categories that are economy sport, entertainment or technology, is taken from wefollow.com application. 2105 RSS news feeds, whose category labels are same with microbloggers' contributions, are used as training data for classification. In this study two types of users' contributions are taken as test data. These users are normal micro loggers and bots. Classification results show that bots provide more categorical content than normal users. |
| doi_str_mv | 10.1109/ASONAM.2012.151 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6425656</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6425656</ieee_id><sourcerecordid>6425656</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-418989d0e509ac38271adf21a6104f354f03e518bbf99a398435ccb84bc6eaad3</originalsourceid><addsrcrecordid>eNotjLFOwzAUAI0QEtBmZoChP5Dwnu1n-41RBAWppUNhrhzHroxKgpIs_D2VYDjdTSfEHUKFCPxY73dv9baSgLJCwgtxC9YwacsMl6Jg61Abq6Rma69FMU2fAICg6MyNeGiGfo79vNrmPvfH1ZDOFcahPQ3HaSmukj9Nsfj3Qnw8P703L-Vmt35t6k2Z0dJcanTsuINIwD4oJy36Lkn0BkEnRTqBioSubROzV-y0ohBap9tgovedWoj7v2-OMR6-x_zlx5-D0ZIMGfUL1Jc8cw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Content Mining of Microblogs</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Cingiz, M. O. ; Diri, B.</creator><creatorcontrib>Cingiz, M. O. ; Diri, B.</creatorcontrib><description>Emergence of Web 2.0, internet users can share their contents with other users using social networks. In this paper microbloggers' contents are evaluated with respect to how they reflect their categories. Migrobloggers' category information, which is one of the four categories that are economy sport, entertainment or technology, is taken from wefollow.com application. 2105 RSS news feeds, whose category labels are same with microbloggers' contributions, are used as training data for classification. In this study two types of users' contributions are taken as test data. These users are normal micro loggers and bots. Classification results show that bots provide more categorical content than normal users.</description><identifier>ISBN: 9781467324977</identifier><identifier>ISBN: 1467324973</identifier><identifier>EISBN: 0769547990</identifier><identifier>EISBN: 9780769547992</identifier><identifier>DOI: 10.1109/ASONAM.2012.151</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>classification ; content mining ; data mining ; Educational institutions ; Entertainment industry ; Feeds ; microblogging ; social web mining ; Support vector machine classification ; Text categorization ; Training</subject><ispartof>2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012, p.835-838</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6425656$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54918</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6425656$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Cingiz, M. O.</creatorcontrib><creatorcontrib>Diri, B.</creatorcontrib><title>Content Mining of Microblogs</title><title>2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining</title><addtitle>asonam</addtitle><description>Emergence of Web 2.0, internet users can share their contents with other users using social networks. In this paper microbloggers' contents are evaluated with respect to how they reflect their categories. Migrobloggers' category information, which is one of the four categories that are economy sport, entertainment or technology, is taken from wefollow.com application. 2105 RSS news feeds, whose category labels are same with microbloggers' contributions, are used as training data for classification. In this study two types of users' contributions are taken as test data. These users are normal micro loggers and bots. Classification results show that bots provide more categorical content than normal users.</description><subject>classification</subject><subject>content mining</subject><subject>data mining</subject><subject>Educational institutions</subject><subject>Entertainment industry</subject><subject>Feeds</subject><subject>microblogging</subject><subject>social web mining</subject><subject>Support vector machine classification</subject><subject>Text categorization</subject><subject>Training</subject><isbn>9781467324977</isbn><isbn>1467324973</isbn><isbn>0769547990</isbn><isbn>9780769547992</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjLFOwzAUAI0QEtBmZoChP5Dwnu1n-41RBAWppUNhrhzHroxKgpIs_D2VYDjdTSfEHUKFCPxY73dv9baSgLJCwgtxC9YwacsMl6Jg61Abq6Rma69FMU2fAICg6MyNeGiGfo79vNrmPvfH1ZDOFcahPQ3HaSmukj9Nsfj3Qnw8P703L-Vmt35t6k2Z0dJcanTsuINIwD4oJy36Lkn0BkEnRTqBioSubROzV-y0ohBap9tgovedWoj7v2-OMR6-x_zlx5-D0ZIMGfUL1Jc8cw</recordid><startdate>201208</startdate><enddate>201208</enddate><creator>Cingiz, M. O.</creator><creator>Diri, B.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201208</creationdate><title>Content Mining of Microblogs</title><author>Cingiz, M. O. ; Diri, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-418989d0e509ac38271adf21a6104f354f03e518bbf99a398435ccb84bc6eaad3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>classification</topic><topic>content mining</topic><topic>data mining</topic><topic>Educational institutions</topic><topic>Entertainment industry</topic><topic>Feeds</topic><topic>microblogging</topic><topic>social web mining</topic><topic>Support vector machine classification</topic><topic>Text categorization</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Cingiz, M. O.</creatorcontrib><creatorcontrib>Diri, B.</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 Electronic Library (IEL)</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>Cingiz, M. O.</au><au>Diri, B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Content Mining of Microblogs</atitle><btitle>2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining</btitle><stitle>asonam</stitle><date>2012-08</date><risdate>2012</risdate><spage>835</spage><epage>838</epage><pages>835-838</pages><isbn>9781467324977</isbn><isbn>1467324973</isbn><eisbn>0769547990</eisbn><eisbn>9780769547992</eisbn><coden>IEEPAD</coden><abstract>Emergence of Web 2.0, internet users can share their contents with other users using social networks. In this paper microbloggers' contents are evaluated with respect to how they reflect their categories. Migrobloggers' category information, which is one of the four categories that are economy sport, entertainment or technology, is taken from wefollow.com application. 2105 RSS news feeds, whose category labels are same with microbloggers' contributions, are used as training data for classification. In this study two types of users' contributions are taken as test data. These users are normal micro loggers and bots. Classification results show that bots provide more categorical content than normal users.</abstract><pub>IEEE</pub><doi>10.1109/ASONAM.2012.151</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781467324977 |
| ispartof | 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012, p.835-838 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_6425656 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | classification content mining data mining Educational institutions Entertainment industry Feeds microblogging social web mining Support vector machine classification Text categorization Training |
| title | Content Mining of Microblogs |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T11%3A00%3A46IST&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=Content%20Mining%20of%20Microblogs&rft.btitle=2012%20IEEE/ACM%20International%20Conference%20on%20Advances%20in%20Social%20Networks%20Analysis%20and%20Mining&rft.au=Cingiz,%20M.%20O.&rft.date=2012-08&rft.spage=835&rft.epage=838&rft.pages=835-838&rft.isbn=9781467324977&rft.isbn_list=1467324973&rft.coden=IEEPAD&rft_id=info:doi/10.1109/ASONAM.2012.151&rft_dat=%3Cieee_6IE%3E6425656%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=0769547990&rft.eisbn_list=9780769547992&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6425656&rfr_iscdi=true |