A Spatio-Temporal Approach to the Discovery of Online Social Trends
Identifieur interne : 001002 ( Istex/Corpus ); précédent : 001001; suivant : 001003A Spatio-Temporal Approach to the Discovery of Online Social Trends
Auteurs : Harshavardhan Achrekar ; Zheng Fang ; You Li ; Cindy Chen ; Benyuan Liu ; Jie WangSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: Online social networks (OSNs) have become popular platforms for people to interact with each other in the cyber space. Users use OSNs to talk about their daily activities, mood, health status, sports events, travel experiences, political campaigns, entertainment events, and commercial products, among other things. Conversations between users on an OSN site could reflect the current social trends that are of great interest and importance for individuals, businesses, and government agencies alike. In this paper we design and develop a comprehensive system to collect, store, query, and analyze OSN data for effective discovery of online social trends. Our system consists of three parts: (1) an OSN data collection engine; (2) a spatio-temporal database for storing, indexing, and querying data; and (3) a set of analytical tools for online social trend discovery. We demonstrate the effectiveness of our system using a recent result of predicting seasonal flu trends using Twitter data.
Url:
DOI: 10.1007/978-3-642-22616-8_40
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ISTEX:9D89A2F3A123D1BBD0B07A34277BC6270F825A96Le document en format XML
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Users use OSNs to talk about their daily activities, mood, health status, sports events, travel experiences, political campaigns, entertainment events, and commercial products, among other things. Conversations between users on an OSN site could reflect the current social trends that are of great interest and importance for individuals, businesses, and government agencies alike. In this paper we design and develop a comprehensive system to collect, store, query, and analyze OSN data for effective discovery of online social trends. Our system consists of three parts: (1) an OSN data collection engine; (2) a spatio-temporal database for storing, indexing, and querying data; and (3) a set of analytical tools for online social trend discovery. We demonstrate the effectiveness of our system using a recent result of predicting seasonal flu trends using Twitter data.</div></front></TEI><istex><corpusName>springer-ebooks</corpusName><author><json:item><name>Harshavardhan Achrekar</name><affiliations><json:string>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</json:string></affiliations></json:item><json:item><name>Zheng Fang</name><affiliations><json:string>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</json:string></affiliations></json:item><json:item><name>You Li</name><affiliations><json:string>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</json:string></affiliations></json:item><json:item><name>Cindy Chen</name><affiliations><json:string>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</json:string></affiliations></json:item><json:item><name>Benyuan Liu</name><affiliations><json:string>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</json:string></affiliations></json:item><json:item><name>Jie Wang</name><affiliations><json:string>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</json:string></affiliations></json:item></author><arkIstex>ark:/67375/HCB-Z4LZDDP7-7</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: Online social networks (OSNs) have become popular platforms for people to interact with each other in the cyber space. Users use OSNs to talk about their daily activities, mood, health status, sports events, travel experiences, political campaigns, entertainment events, and commercial products, among other things. Conversations between users on an OSN site could reflect the current social trends that are of great interest and importance for individuals, businesses, and government agencies alike. In this paper we design and develop a comprehensive system to collect, store, query, and analyze OSN data for effective discovery of online social trends. Our system consists of three parts: (1) an OSN data collection engine; (2) a spatio-temporal database for storing, indexing, and querying data; and (3) a set of analytical tools for online social trend discovery. 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Massachusetts</orgName><address><settlement>Lowell</settlement><region>MA</region><postCode>01854</postCode><country key="US">UNITED STATES</country></address></affiliation></author><author><persName><forename type="first">Zheng</forename><surname>Fang</surname></persName><affiliation><orgName type="department">Department of Computer Science</orgName><orgName type="institution">University of Massachusetts</orgName><address><settlement>Lowell</settlement><region>MA</region><postCode>01854</postCode><country key="US">UNITED STATES</country></address></affiliation></author><author><persName><forename type="first">You</forename><surname>Li</surname></persName><affiliation><orgName type="department">Department of Computer Science</orgName><orgName type="institution">University of Massachusetts</orgName><address><settlement>Lowell</settlement><region>MA</region><postCode>01854</postCode><country key="US">UNITED STATES</country></address></affiliation></author><author><persName><forename type="first">Cindy</forename><surname>Chen</surname></persName><affiliation><orgName type="department">Department of Computer Science</orgName><orgName type="institution">University of Massachusetts</orgName><address><settlement>Lowell</settlement><region>MA</region><postCode>01854</postCode><country key="US">UNITED STATES</country></address></affiliation></author><author><persName><forename type="first">Benyuan</forename><surname>Liu</surname></persName><affiliation><orgName type="department">Department of Computer Science</orgName><orgName type="institution">University of Massachusetts</orgName><address><settlement>Lowell</settlement><region>MA</region><postCode>01854</postCode><country key="US">UNITED STATES</country></address></affiliation></author><author><persName><forename type="first">Jie</forename><surname>Wang</surname></persName><affiliation><orgName type="department">Department of Computer Science</orgName><orgName type="institution">University of 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Proceedings</title><idno type="DOI">10.1007/978-3-642-22616-8</idno><idno type="book-id">978-3-642-22616-8</idno><idno type="ISBN">978-3-642-22615-1</idno><idno type="eISBN">978-3-642-22616-8</idno><idno type="chapter-id">Chap40</idno><editor><persName><forename type="first">Weifan</forename><surname>Wang</surname></persName><email>wwf@zjnu.cn</email><affiliation><orgName type="institution">Zhejiang Normal University</orgName><address><street>688 Yingbin Road</street><postCode>321004</postCode><settlement>Jinhua</settlement><region>Zhejiang Province</region><country key="CN">CHINA</country></address></affiliation></editor><editor><persName><forename type="first">Xuding</forename><surname>Zhu</surname></persName><email>xudingzhu@gmail.com</email><affiliation><orgName type="institution">Zhejiang Normal University</orgName><address><street>688 Yingbin Road</street><postCode>321004</postCode><settlement>Jinhua</settlement><region>Zhejiang Province</region><country 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University</orgName><address><settlement>Lancaster</settlement><country key="GB">UNITED KINGDOM</country></address></affiliation></editor><editor><persName><forename type="first">Takeo</forename><surname>Kanade</surname></persName><affiliation><orgName type="institution">Carnegie Mellon University</orgName><address><settlement>Pittsburgh</settlement><region>PA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Josef</forename><surname>Kittler</surname></persName><affiliation><orgName type="institution">University of Surrey</orgName><address><settlement>Guildford</settlement><country key="GB">UNITED KINGDOM</country></address></affiliation></editor><editor><persName><forename type="first">Jon</forename><forename type="first">M.</forename><surname>Kleinberg</surname></persName><affiliation><orgName type="institution">Cornell University</orgName><address><settlement>Ithaca</settlement><region>NY</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Friedemann</forename><surname>Mattern</surname></persName><affiliation><orgName type="institution">ETH Zurich</orgName><address><settlement>Zurich</settlement><country key="CH">SWITZERLAND</country></address></affiliation></editor><editor><persName><forename type="first">John</forename><forename type="first">C.</forename><surname>Mitchell</surname></persName><affiliation><orgName type="institution">Stanford University</orgName><address><settlement>Stanford</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation><orgName type="institution">Weizmann Institute of Science</orgName><address><settlement>Rehovot</settlement><country key="IL">ISRAEL</country></address></affiliation></editor><editor><persName><forename type="first">Oscar</forename><surname>Nierstrasz</surname></persName><affiliation><orgName type="institution">University of Bern</orgName><address><settlement>Bern</settlement><country key="CH">SWITZERLAND</country></address></affiliation></editor><editor><persName><forename type="first">C.</forename><surname>Pandu Rangan</surname></persName><affiliation><orgName type="institution">Indian Institute of Technology</orgName><address><settlement>Madras</settlement><country key="IN">INDIA</country></address></affiliation></editor><editor><persName><forename type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation><orgName type="institution">University of Dortmund</orgName><address><settlement>Dortmund</settlement><country key="DE">GERMANY</country></address></affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation><orgName type="institution">Massachusetts Institute of Technology</orgName><address><region>MA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation><orgName type="institution">University of California</orgName><address><settlement>Los Angeles</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation><orgName type="institution">University of California</orgName><address><settlement>Berkeley</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation><orgName type="institution">Rice University</orgName><address><settlement>Houston</settlement><region>TX</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation><orgName type="institution">Max-Planck Institute of Computer Science</orgName><address><settlement>Saarbrücken</settlement><country key="DE">GERMANY</country></address></affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="seriesID">558</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><abstract xml:lang="en"><head>Abstract</head><p>Online social networks (OSNs) have become popular platforms for people to interact with each other in the cyber space. Users use OSNs to talk about their daily activities, mood, health status, sports events, travel experiences, political campaigns, entertainment events, and commercial products, among other things. Conversations between users on an OSN site could reflect the current social trends that are of great interest and importance for individuals, businesses, and government agencies alike. In this paper we design and develop a comprehensive system to collect, store, query, and analyze OSN data for effective discovery of online social trends. Our system consists of three parts: (1) an OSN data collection engine; (2) a spatio-temporal database for storing, indexing, and querying data; and (3) a set of analytical tools for online social trend discovery. We demonstrate the effectiveness of our system using a recent result of predicting seasonal flu trends using Twitter data.</p></abstract><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I16021</label><item><term type="Secondary" subtype="priority-1">Algorithm Analysis and Problem Complexity</term></item><label>I17028</label><item><term type="Secondary" subtype="priority-2">Discrete Mathematics in Computer Science</term></item><label>I1701X</label><item><term type="Secondary" subtype="priority-3">Numeric Computing</term></item><label>I13022</label><item><term type="Secondary" subtype="priority-4">Computer Communication Networks</term></item><label>I22013</label><item><term type="Secondary" subtype="priority-5">Computer 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University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><City>Zurich</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-22616-8</BookID><BookTitle>Combinatorial Optimization and Applications</BookTitle><BookSubTitle>5th International Conference, COCOA 2011, Zhangjiajie, China, August 4-6, 2011. Proceedings</BookSubTitle><BookVolumeNumber>6831</BookVolumeNumber><BookSequenceNumber>6831</BookSequenceNumber><BookDOI>10.1007/978-3-642-22616-8</BookDOI><BookTitleID>271949</BookTitleID><BookPrintISBN>978-3-642-22615-1</BookPrintISBN><BookElectronicISBN>978-3-642-22616-8</BookElectronicISBN><BookChapterCount>43</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag GmbH Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I16021" Priority="1" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="I17028" Priority="2" Type="Secondary">Discrete Mathematics in Computer Science</BookSubject><BookSubject Code="I1701X" Priority="3" Type="Secondary">Numeric Computing</BookSubject><BookSubject Code="I13022" Priority="4" Type="Secondary">Computer Communication Networks</BookSubject><BookSubject Code="I22013" Priority="5" Type="Secondary">Computer Graphics</BookSubject><BookSubject Code="M14018" Priority="6" Type="Secondary">Algorithms</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Weifan</GivenName><FamilyName>Wang</FamilyName></EditorName><Contact><Email>wwf@zjnu.cn</Email></Contact></Editor><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Xuding</GivenName><FamilyName>Zhu</FamilyName></EditorName><Contact><Email>xudingzhu@gmail.com</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Ding-Zhu</GivenName><FamilyName>Du</FamilyName></EditorName><Contact><Email>dzdu@utdallas.edu</Email></Contact></Editor><Affiliation ID="Aff16"><OrgName>Zhejiang Normal University</OrgName><OrgAddress><Street>688 Yingbin Road</Street><Postcode>321004</Postcode><City>Jinhua</City><State>Zhejiang Province</State><Country>China</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Department of Computer Science</OrgDivision><OrgName>University of Texas at Dallas</OrgName><OrgAddress><Postcode>75080</Postcode><City>Richardson</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Chapter ID="Chap40" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>40</ChapterID><ChapterDOI>10.1007/978-3-642-22616-8_40</ChapterDOI><ChapterSequenceNumber>40</ChapterSequenceNumber><ChapterTitle Language="En">A Spatio-Temporal Approach to the Discovery of Online Social Trends</ChapterTitle><ChapterFirstPage>510</ChapterFirstPage><ChapterLastPage>524</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><BookID>978-3-642-22616-8</BookID><BookTitle>Combinatorial Optimization and Applications</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Harshavardhan</GivenName><FamilyName>Achrekar</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Zheng</GivenName><FamilyName>Fang</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>You</GivenName><FamilyName>Li</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Cindy</GivenName><FamilyName>Chen</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Benyuan</GivenName><FamilyName>Liu</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Jie</GivenName><FamilyName>Wang</FamilyName></AuthorName></Author><Affiliation ID="Aff18"><OrgDivision>Department of Computer Science</OrgDivision><OrgName>University of Massachusetts</OrgName><OrgAddress><City>Lowell</City><State>MA</State><Postcode>01854</Postcode><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Online social networks (OSNs) have become popular platforms for people to interact with each other in the cyber space. Users use OSNs to talk about their daily activities, mood, health status, sports events, travel experiences, political campaigns, entertainment events, and commercial products, among other things. Conversations between users on an OSN site could reflect the current social trends that are of great interest and importance for individuals, businesses, and government agencies alike. In this paper we design and develop a comprehensive system to collect, store, query, and analyze OSN data for effective discovery of online social trends. Our system consists of three parts: (1) an OSN data collection engine; (2) a spatio-temporal database for storing, indexing, and querying data; and (3) a set of analytical tools for online social trend discovery. We demonstrate the effectiveness of our system using a recent result of predicting seasonal flu trends using Twitter data.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Spatio-Temporal Approach to the Discovery of Online Social Trends</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>A Spatio-Temporal Approach to the Discovery of Online Social Trends</title></titleInfo><name type="personal"><namePart type="given">Harshavardhan</namePart><namePart type="family">Achrekar</namePart><affiliation>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Zheng</namePart><namePart type="family">Fang</namePart><affiliation>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">You</namePart><namePart type="family">Li</namePart><affiliation>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Cindy</namePart><namePart type="family">Chen</namePart><affiliation>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Benyuan</namePart><namePart type="family">Liu</namePart><affiliation>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jie</namePart><namePart type="family">Wang</namePart><affiliation>Department of Computer Science, University of Massachusetts, 01854, Lowell, MA, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: Online social networks (OSNs) have become popular platforms for people to interact with each other in the cyber space. Users use OSNs to talk about their daily activities, mood, health status, sports events, travel experiences, political campaigns, entertainment events, and commercial products, among other things. Conversations between users on an OSN site could reflect the current social trends that are of great interest and importance for individuals, businesses, and government agencies alike. In this paper we design and develop a comprehensive system to collect, store, query, and analyze OSN data for effective discovery of online social trends. Our system consists of three parts: (1) an OSN data collection engine; (2) a spatio-temporal database for storing, indexing, and querying data; and (3) a set of analytical tools for online social trend discovery. 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