Loading…
Using dynamic community detection to identify trends in user-generated content
In this paper, we present a new solution for trend detection in user-generated content, and more particularly Web 2.0 social networks. Whereas some propositions have been published in this domain recently, we have chosen a new approach based on network analysis. We first create an evolving network o...
Saved in:
Published in: | Social network analysis and mining 2012-12, Vol.2 (4), p.361-371 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c312t-893918e82196fb0eb79cc3d2263e2036870c84f0515d88b579b2f71713718b603 |
---|---|
cites | cdi_FETCH-LOGICAL-c312t-893918e82196fb0eb79cc3d2263e2036870c84f0515d88b579b2f71713718b603 |
container_end_page | 371 |
container_issue | 4 |
container_start_page | 361 |
container_title | Social network analysis and mining |
container_volume | 2 |
creator | Cazabet, Rémy Takeda, Hideaki Hamasaki, Masahiro Amblard, Frédéric |
description | In this paper, we present a new solution for trend detection in user-generated content, and more particularly Web 2.0 social networks. Whereas some propositions have been published in this domain recently, we have chosen a new approach based on network analysis. We first create an evolving network of terms, which is an abstraction of the complete network, and then run a dynamic community detection algorithm on this evolving network. In order to be able to detect not only short, bursting events, but also more persistent topics, we test our solution on a social network for which we have information about all published contents for a period of more than 2 years: the Japanese network Nico Nico Douga. After presenting our solution in detail, we present the results on this dataset, notably a statistical analysis of communities’ sizes and durations, examples of detected communities, and a typology of the different kinds of trends detected. Finally, we discuss the advantages and disadvantages of this method, as well as its possible applications. |
doi_str_mv | 10.1007/s13278-012-0074-8 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2919610452</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2919610452</sourcerecordid><originalsourceid>FETCH-LOGICAL-c312t-893918e82196fb0eb79cc3d2263e2036870c84f0515d88b579b2f71713718b603</originalsourceid><addsrcrecordid>eNp1kE1LxDAQhoMouOj-AG8Bz9WZpG2Soyx-waIX9xzaNF2y2HRN0kP_vVkqevI0M_C878BDyA3CHQKI-4icCVkAsiKfZSHPyAplrYqqrNX5717BJVnHeAAABM4V1CvytovO72k3-2ZwhppxGCbv0kw7m6xJbvQ0jdR11ifXzzQF67tInadTtKHYW29Dk2yXgz5l5ppc9M1ntOufeUV2T48fm5di-_78unnYFoYjS4VUXKG0kqGq-xZsK5QxvGOs5pYBr6UAI8seKqw6KdtKqJb1AgVygbKtgV-R26X3GMavycakD-MUfH6pmcqlCGXFMoULZcIYY7C9PgY3NGHWCPpkTi_mdDanT-a0zBm2ZGJm_d6Gv-b_Q9-j-m96</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2919610452</pqid></control><display><type>article</type><title>Using dynamic community detection to identify trends in user-generated content</title><source>International Bibliography of the Social Sciences (IBSS)</source><source>Social Science Premium Collection</source><source>Springer Nature</source><creator>Cazabet, Rémy ; Takeda, Hideaki ; Hamasaki, Masahiro ; Amblard, Frédéric</creator><creatorcontrib>Cazabet, Rémy ; Takeda, Hideaki ; Hamasaki, Masahiro ; Amblard, Frédéric</creatorcontrib><description>In this paper, we present a new solution for trend detection in user-generated content, and more particularly Web 2.0 social networks. Whereas some propositions have been published in this domain recently, we have chosen a new approach based on network analysis. We first create an evolving network of terms, which is an abstraction of the complete network, and then run a dynamic community detection algorithm on this evolving network. In order to be able to detect not only short, bursting events, but also more persistent topics, we test our solution on a social network for which we have information about all published contents for a period of more than 2 years: the Japanese network Nico Nico Douga. After presenting our solution in detail, we present the results on this dataset, notably a statistical analysis of communities’ sizes and durations, examples of detected communities, and a typology of the different kinds of trends detected. Finally, we discuss the advantages and disadvantages of this method, as well as its possible applications.</description><identifier>ISSN: 1869-5450</identifier><identifier>EISSN: 1869-5469</identifier><identifier>DOI: 10.1007/s13278-012-0074-8</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Algorithms ; Applications of Graph Theory and Complex Networks ; Community ; Computer Science ; Data Mining and Knowledge Discovery ; Datasets ; Economics ; Evolution ; Game Theory ; Humanities ; Keywords ; Law ; Methodology of the Social Sciences ; Methods ; Network analysis ; Ontology ; Original Article ; Soccer ; Social and Behav. Sciences ; Social networks ; Statistical analysis ; Statistics for Social Sciences ; Tagging ; Trends ; User generated content ; Web 2.0</subject><ispartof>Social network analysis and mining, 2012-12, Vol.2 (4), p.361-371</ispartof><rights>Springer-Verlag 2012</rights><rights>Springer-Verlag 2012.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c312t-893918e82196fb0eb79cc3d2263e2036870c84f0515d88b579b2f71713718b603</citedby><cites>FETCH-LOGICAL-c312t-893918e82196fb0eb79cc3d2263e2036870c84f0515d88b579b2f71713718b603</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2919610452?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,12826,21373,27901,27902,33200,33588,43709</link.rule.ids></links><search><creatorcontrib>Cazabet, Rémy</creatorcontrib><creatorcontrib>Takeda, Hideaki</creatorcontrib><creatorcontrib>Hamasaki, Masahiro</creatorcontrib><creatorcontrib>Amblard, Frédéric</creatorcontrib><title>Using dynamic community detection to identify trends in user-generated content</title><title>Social network analysis and mining</title><addtitle>Soc. Netw. Anal. Min</addtitle><description>In this paper, we present a new solution for trend detection in user-generated content, and more particularly Web 2.0 social networks. Whereas some propositions have been published in this domain recently, we have chosen a new approach based on network analysis. We first create an evolving network of terms, which is an abstraction of the complete network, and then run a dynamic community detection algorithm on this evolving network. In order to be able to detect not only short, bursting events, but also more persistent topics, we test our solution on a social network for which we have information about all published contents for a period of more than 2 years: the Japanese network Nico Nico Douga. After presenting our solution in detail, we present the results on this dataset, notably a statistical analysis of communities’ sizes and durations, examples of detected communities, and a typology of the different kinds of trends detected. Finally, we discuss the advantages and disadvantages of this method, as well as its possible applications.</description><subject>Algorithms</subject><subject>Applications of Graph Theory and Complex Networks</subject><subject>Community</subject><subject>Computer Science</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Datasets</subject><subject>Economics</subject><subject>Evolution</subject><subject>Game Theory</subject><subject>Humanities</subject><subject>Keywords</subject><subject>Law</subject><subject>Methodology of the Social Sciences</subject><subject>Methods</subject><subject>Network analysis</subject><subject>Ontology</subject><subject>Original Article</subject><subject>Soccer</subject><subject>Social and Behav. Sciences</subject><subject>Social networks</subject><subject>Statistical analysis</subject><subject>Statistics for Social Sciences</subject><subject>Tagging</subject><subject>Trends</subject><subject>User generated content</subject><subject>Web 2.0</subject><issn>1869-5450</issn><issn>1869-5469</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><sourceid>ALSLI</sourceid><sourceid>M2R</sourceid><recordid>eNp1kE1LxDAQhoMouOj-AG8Bz9WZpG2Soyx-waIX9xzaNF2y2HRN0kP_vVkqevI0M_C878BDyA3CHQKI-4icCVkAsiKfZSHPyAplrYqqrNX5717BJVnHeAAABM4V1CvytovO72k3-2ZwhppxGCbv0kw7m6xJbvQ0jdR11ifXzzQF67tInadTtKHYW29Dk2yXgz5l5ppc9M1ntOufeUV2T48fm5di-_78unnYFoYjS4VUXKG0kqGq-xZsK5QxvGOs5pYBr6UAI8seKqw6KdtKqJb1AgVygbKtgV-R26X3GMavycakD-MUfH6pmcqlCGXFMoULZcIYY7C9PgY3NGHWCPpkTi_mdDanT-a0zBm2ZGJm_d6Gv-b_Q9-j-m96</recordid><startdate>20121201</startdate><enddate>20121201</enddate><creator>Cazabet, Rémy</creator><creator>Takeda, Hideaki</creator><creator>Hamasaki, Masahiro</creator><creator>Amblard, Frédéric</creator><general>Springer Vienna</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0-V</scope><scope>3V.</scope><scope>7XB</scope><scope>88J</scope><scope>8BJ</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JBE</scope><scope>JQ2</scope><scope>K7-</scope><scope>M2R</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>20121201</creationdate><title>Using dynamic community detection to identify trends in user-generated content</title><author>Cazabet, Rémy ; Takeda, Hideaki ; Hamasaki, Masahiro ; Amblard, Frédéric</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c312t-893918e82196fb0eb79cc3d2263e2036870c84f0515d88b579b2f71713718b603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>Applications of Graph Theory and Complex Networks</topic><topic>Community</topic><topic>Computer Science</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Datasets</topic><topic>Economics</topic><topic>Evolution</topic><topic>Game Theory</topic><topic>Humanities</topic><topic>Keywords</topic><topic>Law</topic><topic>Methodology of the Social Sciences</topic><topic>Methods</topic><topic>Network analysis</topic><topic>Ontology</topic><topic>Original Article</topic><topic>Soccer</topic><topic>Social and Behav. Sciences</topic><topic>Social networks</topic><topic>Statistical analysis</topic><topic>Statistics for Social Sciences</topic><topic>Tagging</topic><topic>Trends</topic><topic>User generated content</topic><topic>Web 2.0</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cazabet, Rémy</creatorcontrib><creatorcontrib>Takeda, Hideaki</creatorcontrib><creatorcontrib>Hamasaki, Masahiro</creatorcontrib><creatorcontrib>Amblard, Frédéric</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Social Science Database (Alumni Edition)</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Social Science Database (ProQuest)</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Social network analysis and mining</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cazabet, Rémy</au><au>Takeda, Hideaki</au><au>Hamasaki, Masahiro</au><au>Amblard, Frédéric</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using dynamic community detection to identify trends in user-generated content</atitle><jtitle>Social network analysis and mining</jtitle><stitle>Soc. Netw. Anal. Min</stitle><date>2012-12-01</date><risdate>2012</risdate><volume>2</volume><issue>4</issue><spage>361</spage><epage>371</epage><pages>361-371</pages><issn>1869-5450</issn><eissn>1869-5469</eissn><abstract>In this paper, we present a new solution for trend detection in user-generated content, and more particularly Web 2.0 social networks. Whereas some propositions have been published in this domain recently, we have chosen a new approach based on network analysis. We first create an evolving network of terms, which is an abstraction of the complete network, and then run a dynamic community detection algorithm on this evolving network. In order to be able to detect not only short, bursting events, but also more persistent topics, we test our solution on a social network for which we have information about all published contents for a period of more than 2 years: the Japanese network Nico Nico Douga. After presenting our solution in detail, we present the results on this dataset, notably a statistical analysis of communities’ sizes and durations, examples of detected communities, and a typology of the different kinds of trends detected. Finally, we discuss the advantages and disadvantages of this method, as well as its possible applications.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s13278-012-0074-8</doi><tpages>11</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1869-5450 |
ispartof | Social network analysis and mining, 2012-12, Vol.2 (4), p.361-371 |
issn | 1869-5450 1869-5469 |
language | eng |
recordid | cdi_proquest_journals_2919610452 |
source | International Bibliography of the Social Sciences (IBSS); Social Science Premium Collection; Springer Nature |
subjects | Algorithms Applications of Graph Theory and Complex Networks Community Computer Science Data Mining and Knowledge Discovery Datasets Economics Evolution Game Theory Humanities Keywords Law Methodology of the Social Sciences Methods Network analysis Ontology Original Article Soccer Social and Behav. Sciences Social networks Statistical analysis Statistics for Social Sciences Tagging Trends User generated content Web 2.0 |
title | Using dynamic community detection to identify trends in user-generated content |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T12%3A55%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Using%20dynamic%20community%20detection%20to%20identify%20trends%20in%20user-generated%20content&rft.jtitle=Social%20network%20analysis%20and%20mining&rft.au=Cazabet,%20R%C3%A9my&rft.date=2012-12-01&rft.volume=2&rft.issue=4&rft.spage=361&rft.epage=371&rft.pages=361-371&rft.issn=1869-5450&rft.eissn=1869-5469&rft_id=info:doi/10.1007/s13278-012-0074-8&rft_dat=%3Cproquest_cross%3E2919610452%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c312t-893918e82196fb0eb79cc3d2263e2036870c84f0515d88b579b2f71713718b603%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2919610452&rft_id=info:pmid/&rfr_iscdi=true |