Identifying Influentials in Social Networks
In recent years, social networks have become very popular and an integral part of everyday life. People express their feelings and experiences in this virtual environment and become aware of others' opinions and interests. Among them, influential users play an important role in disseminating in...
Saved in:
Published in | Applied artificial intelligence Vol. 36; no. 1 |
---|---|
Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Philadelphia
Taylor & Francis
31.12.2022
Taylor & Francis Ltd Taylor & Francis Group |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | In recent years, social networks have become very popular and an integral part of everyday life. People express their feelings and experiences in this virtual environment and become aware of others' opinions and interests. Among them, influential users play an important role in disseminating information on social networks. Identifying such influencers is important in designing techniques to increase the speed of information dissemination. Such techniques are applicable in various fields including viral marketing, preventing the dissemination of harmful information, providing specialized recommendations, etc. Various approaches have been used to detect influencers on social networks, mostly based on the individual's position in the network structure and their interactions. Considering the strengths and weaknesses of the previous methods, this study presents a novel method based on the content of the users' posts without considering the network structure. This is done using a combination of high-level features extracted from images to measure the individual's influence. Users' images are investigated from three aspects: (1) color scheme, (2) advertising nature, (3) images' semantics. To describe each of these aspects, feature extraction methods were used with acceptable accuracy in recognizing influential users. Finally, to achieve greater efficiency and precision, feature-combination methods have been investigated to provide an integrated classifier. |
---|---|
AbstractList | In recent years, social networks have become very popular and an integral part of everyday life. People express their feelings and experiences in this virtual environment and become aware of others’ opinions and interests. Among them, influential users play an important role in disseminating information on social networks. Identifying such influencers is important in designing techniques to increase the speed of information dissemination. Such techniques are applicable in various fields including viral marketing, preventing the dissemination of harmful information, providing specialized recommendations, etc. Various approaches have been used to detect influencers on social networks, mostly based on the individual’s position in the network structure and their interactions. Considering the strengths and weaknesses of the previous methods, this study presents a novel method based on the content of the users’ posts without considering the network structure. This is done using a combination of high-level features extracted from images to measure the individual’s influence. Users’ images are investigated from three aspects: (1) color scheme, (2) advertising nature, (3) images’ semantics. To describe each of these aspects, feature extraction methods were used with acceptable accuracy in recognizing influential users. Finally, to achieve greater efficiency and precision, feature-combination methods have been investigated to provide an integrated classifier. |
Author | Abbasi, Fatemeh Fazl-Ersi, Ehsan |
Author_xml | – sequence: 1 givenname: Fatemeh surname: Abbasi fullname: Abbasi, Fatemeh organization: Ferdowsi University of Mashhad – sequence: 2 givenname: Ehsan surname: Fazl-Ersi fullname: Fazl-Ersi, Ehsan email: fazlersi@um.ac.ir organization: Ferdowsi University of Mashhad |
BookMark | eNp9kMtKw0AUhgepYFt9BKHgUlLnnmSnFC-Bogt1PZzMpaSmM3WSIn17J7a6dHOu__ln-CZo5IO3CF0SPCe4wDe4KFgpCJ9TTEkKaVjIEzROOc-k4GKExoMmG0RnaNJ1a4wxyXMyRteVsb5v3L7xq1nlXbsbWmi7WeNnr0GncvZs-68QP7pzdOrSxl4c8xS9P9y_LZ6y5ctjtbhbZpoL0me2LDGmBqg1RoPMwei8YMAEcYYXpayBW5CMG1GXddrVlNW5BNCScC6lZFNUHXxNgLXaxmYDca8CNOpnEOJKQewb3VoFjhbUCIZFzTnXMvVYQu2gqHMuHUteVwevbQyfO9v1ah120afvK5oLLgUVjCaVOKh0DF0Xrft7lWA1MFa_jNXAWB0Zp7vbw13jXYgbSJhao3rYtyG6CF43nWL_W3wD-ZmDfQ |
CitedBy_id | crossref_primary_10_1007_s11280_023_01192_w crossref_primary_10_25629_HC_2024_03_26 |
Cites_doi | 10.1109/CVPR.2017.123 10.5121/ijma.2015.7103 10.1016/j.ipm.2016.04.003 10.1145/1772690.1772751 10.1016/j.eswa.2014.11.006 10.1016/j.physa.2011.09.017 10.1016/j.physa.2013.10.047 10.1016/j.physa.2016.08.041 10.1177/0020294019877489 10.1016/j.physa.2016.02.028 10.1016/j.physa.2015.03.042 10.1016/j.physa.2015.12.162 10.1109/TIP.2009.2019809 10.1016/j.physa.2014.02.041 10.1145/2659889 10.1016/j.procs.2015.05.428 10.4304/jnw.8.11.2649-2655 10.1109/34.667881 10.1016/j.physleta.2013.02.039 10.1145/2872518.2889368 10.1109/ACCESS.2017.2672680 10.1016/j.physa.2014.02.032 10.1038/ncomms10168 10.1145/1718487.1718520 10.1145/2502081.2502282 10.1371/journal.pone.0021202 10.1016/j.neucom.2016.11.054 10.1007/s13173-011-0051-5 10.1613/jair.4200 10.1007/978-3-642-45392-2_7 |
ContentType | Journal Article |
Copyright | 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. 2022 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. 2022 – notice: 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | 0YH AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D DOA |
DOI | 10.1080/08839514.2021.2010886 |
DatabaseName | Taylor & Francis Open Access CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals |
DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
DatabaseTitleList | Computer and Information Systems Abstracts |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 0YH name: Taylor & Francis Open Access url: https://www.tandfonline.com sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Computer Science |
EISSN | 1087-6545 |
ExternalDocumentID | oai_doaj_org_article_af282d5305b444c6af206abfa8b746f3 10_1080_08839514_2021_2010886 2010886 |
Genre | Research Article |
GroupedDBID | .4S .7F .DC .QJ 0R~ 0YH 23M 2DF 30N 3YN 4.4 5GY 5VS 8VB AAAVI AAENE AAJMT ABCCY ABDBF ABFIM ABHAV ABIVO ABJVF ABPEM ABPTK ABQHQ ABTAI ACGEJ ACGFS ACGOD ACNCT ACTIO ADCVX ADXPE AEGYZ AEISY AENEX AEOZL AEPSL AEYOC AFKVX AFOLD AGMYJ AHDLD AIJEM AIRXU AJWEG AKVCP ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH ARCSS AVBZW AWYRJ BLEHA CCCUG CE4 CS3 DGEBU DKSSO EAP EBR EBS EBU ECS EDO EMK EPL EST ESX E~A E~B F5P FUNRP FVPDL GROUPED_DOAJ GTTXZ H13 HF~ HZ~ H~9 H~P I-F J.P KYCEM LJTGL M4Z MK~ NA5 NX~ O9- P2P PQQKQ QWB RIG S-T SNACF TFL TFT TFW TH9 TNC TTHFI TUS TWF UT5 UU3 V1K ZL0 ~S~ AAYXX AEMOZ CITATION K1G 7SC 8FD JQ2 L7M L~C L~D |
ID | FETCH-LOGICAL-c451t-e99002da2eddca67adc783a351fd4896ba4ea634d5b9bdc7b23b76aac61446663 |
IEDL.DBID | DOA |
ISSN | 0883-9514 |
IngestDate | Fri Oct 04 13:08:03 EDT 2024 Thu Oct 10 17:23:23 EDT 2024 Fri Aug 23 00:58:00 EDT 2024 Thu Oct 12 04:46:15 EDT 2023 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 1 |
Language | English |
License | open-access: http://creativecommons.org/licenses/by-nc/4.0/: http://creativecommons.org/licenses/by-nc/4.0/: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c451t-e99002da2eddca67adc783a351fd4896ba4ea634d5b9bdc7b23b76aac61446663 |
OpenAccessLink | https://doaj.org/article/af282d5305b444c6af206abfa8b746f3 |
PQID | 2754652532 |
PQPubID | 53050 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_af282d5305b444c6af206abfa8b746f3 informaworld_taylorfrancis_310_1080_08839514_2021_2010886 crossref_primary_10_1080_08839514_2021_2010886 proquest_journals_2754652532 |
PublicationCentury | 2000 |
PublicationDate | 2022-12-31 |
PublicationDateYYYYMMDD | 2022-12-31 |
PublicationDate_xml | – month: 12 year: 2022 text: 2022-12-31 day: 31 |
PublicationDecade | 2020 |
PublicationPlace | Philadelphia |
PublicationPlace_xml | – name: Philadelphia |
PublicationTitle | Applied artificial intelligence |
PublicationYear | 2022 |
Publisher | Taylor & Francis Taylor & Francis Ltd Taylor & Francis Group |
Publisher_xml | – name: Taylor & Francis – name: Taylor & Francis Ltd – name: Taylor & Francis Group |
References | cit0011 cit0012 cit0031 cit0010 cit0030 Cha M. (cit0006) 2010; 14 cit0019 cit0017 cit0018 cit0015 cit0016 cit0013 cit0014 cit0022 cit0001 cit0023 cit0020 cit0021 cit0008 cit0009 cit0028 cit0007 cit0029 cit0004 cit0026 cit0005 cit0027 cit0002 cit0024 cit0003 cit0025 |
References_xml | – ident: cit0010 doi: 10.1109/CVPR.2017.123 – ident: cit0018 doi: 10.5121/ijma.2015.7103 – ident: cit0023 doi: 10.1016/j.ipm.2016.04.003 – ident: cit0013 doi: 10.1145/1772690.1772751 – ident: cit0022 doi: 10.1016/j.eswa.2014.11.006 – ident: cit0007 doi: 10.1016/j.physa.2011.09.017 – volume: 14 start-page: 10 volume-title: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media year: 2010 ident: cit0006 contributor: fullname: Cha M. – ident: cit0001 doi: 10.1016/j.physa.2013.10.047 – ident: cit0021 doi: 10.1016/j.physa.2016.08.041 – ident: cit0002 doi: 10.1177/0020294019877489 – ident: cit0015 doi: 10.1016/j.physa.2016.02.028 – ident: cit0030 doi: 10.1016/j.physa.2015.03.042 – ident: cit0014 doi: 10.1016/j.physa.2015.12.162 – ident: cit0027 doi: 10.1109/TIP.2009.2019809 – ident: cit0020 doi: 10.1016/j.physa.2014.02.041 – ident: cit0024 doi: 10.1145/2659889 – ident: cit0019 doi: 10.1016/j.procs.2015.05.428 – ident: cit0029 doi: 10.4304/jnw.8.11.2649-2655 – ident: cit0012 doi: 10.1109/34.667881 – ident: cit0031 doi: 10.1016/j.physleta.2013.02.039 – ident: cit0026 doi: 10.1145/2872518.2889368 – ident: cit0011 doi: 10.1109/ACCESS.2017.2672680 – ident: cit0008 doi: 10.1016/j.physa.2014.02.032 – ident: cit0016 doi: 10.1038/ncomms10168 – ident: cit0028 doi: 10.1145/1718487.1718520 – ident: cit0005 doi: 10.1145/2502081.2502282 – ident: cit0017 doi: 10.1371/journal.pone.0021202 – ident: cit0025 doi: 10.1016/j.neucom.2016.11.054 – ident: cit0004 doi: 10.1007/s13173-011-0051-5 – ident: cit0009 doi: 10.1613/jair.4200 – ident: cit0003 doi: 10.1007/978-3-642-45392-2_7 |
SSID | ssj0001771 |
Score | 2.3694751 |
Snippet | In recent years, social networks have become very popular and an integral part of everyday life. People express their feelings and experiences in this virtual... |
SourceID | doaj proquest crossref informaworld |
SourceType | Open Website Aggregation Database Publisher |
SubjectTerms | Feature extraction Information dissemination Semantics Social networks Virtual environments |
SummonAdditionalLinks | – databaseName: Taylor & Francis Open Access dbid: 0YH link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV05T8MwFLagLCzciEJBGdhQwLeTERBVQaITlWCy7NhGLAU14f_jKxWHEANjDkfR53da730PgFNUNQIb5bymYVvSplalMhb5nMdAqBmhNo5OuJ_yyYzePbK-mrDNZZUhh3aJKCLa6qDcSrd9RdyFVwziA4NwIoJRrM6qKr4K1rAPvYOow6fJ0hgjEXOusKQMa_omnt8-88U9RRb_bxymP2x2dETjLbCRI8jiMm35Nlix8x2w2U9nKLKy7oKz1IMb-5iK2zSLpAvSVrzMi9SVW0xTEXi7B2bjm4frSZlHI5QNZagrrXci0GOMrTGN4kKZRlREEYacoVXNtaJWcUIN07X2zzQmWnClmpj_-ShjHwzmr3N7AAqEGsW099Mi1OxRqBF2SsDKQqKZFXAIzntE5FtiwJCoJxbNEMoAocwQDsFVwG35ciCwjjdeF88y64NUzud6hnlroymlDffXkCvtVKUF5Y4MQf0ZddnF8wuXho1I8scPjPotklkjW-mFg3KGGcGH__j0EVjHof8hMj2OwKBbvNtjH5V0-iTK3Qft3tWl priority: 102 providerName: Taylor & Francis |
Title | Identifying Influentials in Social Networks |
URI | https://www.tandfonline.com/doi/abs/10.1080/08839514.2021.2010886 https://www.proquest.com/docview/2754652532 https://doaj.org/article/af282d5305b444c6af206abfa8b746f3 |
Volume | 36 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELagEwtvRKFUGdhQwG8nIyCqgkQnKpXJsmNHYimIhv_P-RFUwdCFMU4G6zuf7y66-z6ELknVKOpMC55Gfcmb2pTGeQI1j8PYCsZ9lE54nsnpnD8txGJN6iv0hCV64ATcjWmhKHACjqXlnDcSnrE0tjWVVVy2ieeTiL6YyncwUbHUAhdiJeQQvJ_dCazasBaWoDakJPZ2VWGQei0qRfL-X9Slf67qGH8m-2g3J47FbdrwAdryy0O014syFNlHj9BVGr2N40vFY5Ig6cIhK96WRRrGLWap93t1jOaTh5f7aZkVEcqGC9KVHmIHBmipd64xUhnXqIoZJkjreFVLa7g3knEnbG3hnaXMKmlME8s-SC5O0GD5vvSnqCCkMcJCeFahVY9jS2hrFK48ZlZ4hYfoukdEfyTiC016PtEMoQ4Q6gzhEN0F3H4-DrzVcQGsqbM19SZrDlG9jrru4m-LNmmMaLZhA6PeRDo74kpTFdTeqWD07D_2d452aJh_iEyPIzToPr_8BWQlnR2jbfw6Hcdj-A2zgtof |
link.rule.ids | 315,786,790,870,2115,27533,27955,27956,59496,59497,60239,61028 |
linkProvider | Directory of Open Access Journals |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV05T8MwFLagDLBwI8qZgQ0FfDsZAVGl0HZqpTJZduwglhS14f9jO0lVQIiBMYltRZ_9Luu97wFwhZJcYKMKJ2nYxjRPVayMRS7mMRBqRqgNrROGI55N6NOUTVdqYXxapY-hi5ooIuhqL9z-MrpNibt1kkGcZ-CvRDAK6VlJwtfBBktp6gMw-JIttTESIejyU2I_p63i-W2ZL_Yp0Ph_IzH9obSDJertgu3GhYzu6j3fA2u23Ac7bXuGqJHWA3BdF-GGQqaoXzcjqfxxi97KqC7LjUZ1FvjiEEx6j-OHLG56I8Q5ZaiKrbMi0IGMrTG54kKZXCREEYYKQ5OUa0Wt4oQaplPtvmlMtOBK5SEAdG7GEeiUs9IegwihXDHtDLXwSXsUaoQLJWBiIdHMCtgFNy0i8r2mwJCoZRZtIJQeQtlA2AX3HrflYM9gHV7M5q-yEQipChfsGebUjaaU5tw9Q650oRItKC9IF6SrqMsqXGAUdbcRSf74gbN2i2QjkguJhe_7jhnBJ_9Y-hJsZuPhQA76o-dTsIV9MUSgfTwDnWr-Yc-di1Lpi3AGPwGk89kb |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JT9wwFH4qg1Rx6UAL6rC0OXBDmXp35sg2ggKjHkDqzfKKqkoDYsKFX4-XBFEqxIFjEtuKn99qvfc9gF3cWEmcDlHSiK-ZnehaO49jzOMQMpwyn1snXMzEyRX7-Zv32YSLLq0yxdChAEVkXZ2E-9aFPiPuRxQMGh2DdCNCcM7OahqxBMsiGctUxYFmT8oYyxxzpSl1mtMX8by2zD_mKaP4v8Aw_U9nZ0M0HYLpt1DyT_6O71sztg8v0B3ftcdV-NS5qdV-4as1-ODnn2HYt4CoOo3wBfZKoW8ulqpOS8OTNrF09WdeldLfalYyzRfrcDU9vjw8qbv-C7VlHLe1j5YKxYMk3jmrhdTOyoZqynFwrJkIo5nXgjLHzcTEb4ZQI4XWNgeZ0ZXZgMH8Zu6_QoWx1dxEZ0CmxECGDCZBS9R4RA33Eo1g3JNd3RaYDYV79NKOFCqRQnWkGMFBOpynwQklO7-4ubtWndApHWJA6XhUaYYxZkV8RkKboBsjmQh0BJPnR6vafEkSSkcTRd_4ge2eD1Qn9gtFZOotTzglm-9Y-jt8_HU0Veens7MtWCGp3iIjS27DoL279zvRC2rNt8znj-0R974 |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Identifying+Influentials+in+Social+Networks&rft.jtitle=Applied+artificial+intelligence&rft.au=Abbasi%2C+Fatemeh&rft.au=Fazl-Ersi%2C+Ehsan&rft.date=2022-12-31&rft.pub=Taylor+%26+Francis+Ltd&rft.issn=0883-9514&rft.eissn=1087-6545&rft.volume=36&rft.issue=1&rft_id=info:doi/10.1080%2F08839514.2021.2010886&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0883-9514&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0883-9514&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0883-9514&client=summon |