Tracking the digital footprints to scholarly articles from social media
Scholarly articles are discussed and shared on social media, which generates altmetrics. On the opposite side, what is the impact of social media on the dissemination of scholarly articles and how to measure it? What are the visiting patterns? Investigating these issues, the purpose of this study is...
Gespeichert in:
Veröffentlicht in: | Scientometrics 2016-11, Vol.109 (2), p.1365-1376 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1376 |
---|---|
container_issue | 2 |
container_start_page | 1365 |
container_title | Scientometrics |
container_volume | 109 |
creator | Wang, Xianwen Fang, Zhichao Guo, Xinhui |
description | Scholarly articles are discussed and shared on social media, which generates altmetrics. On the opposite side, what is the impact of social media on the dissemination of scholarly articles and how to measure it? What are the visiting patterns? Investigating these issues, the purpose of this study is to seek a solution to fill the research gap, specifically, to explore the dynamic visiting patterns directed by social media, and examine the effects of social buzz on the article visits. Using the unique real referral data of 110 scholarly articles, which are daily updated in a 90-day period, this paper proposes a novel method to make analysis. We find that visits from social media are fast to accumulate but decay rapidly. Twitter and Facebook are the two most important social referrals that directing people to scholarly articles, the two are about the same and account for over 95 % of the total social referral directed visits. There is synchronism between tweets and tweets resulted visits. Social media and open access are playing important roles in disseminating scholarly articles and promoting public understanding science, which are confirmed quantitatively for the first time with real data in this study. |
doi_str_mv | 10.1007/s11192-016-2086-z |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1880853536</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1880853536</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-d276b5d686c8b9b6420ef3de051ba658cbabb5469f55915563993afe0107cb543</originalsourceid><addsrcrecordid>eNp1kMFOAjEQhhujiYg-gLcmnqszW1raoyGKJiRe8Nx0u11YXCi25QBPb8l68OJpDvN__2Q-Qu4RHhFg-pQQUVcMULIKlGSnCzJCoRSrlMRLMgLkimnkcE1uUtpAYTioEZkvo3Vf3W5F89rTplt12fa0DSHvY7fLieZAk1uH3sb-SG3Mnet9om0MW5qC60p465vO3pKr1vbJ3_3OMfl8fVnO3tjiY_4-e14wx4XOrKmmshaNVNKpWtdyUoFveeNBYG2lUK62dS0mUrdCaBRCcq25bT0gTF1Z8DF5GHr3MXwffMpmEw5xV04aVAqU4ILLksIh5WJIKfrWlG-2Nh4Ngjn7MoMvU3yZsy9zKkw1MOn8-crHP83_Qj8DXW4A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1880853536</pqid></control><display><type>article</type><title>Tracking the digital footprints to scholarly articles from social media</title><source>SpringerLink Journals - AutoHoldings</source><creator>Wang, Xianwen ; Fang, Zhichao ; Guo, Xinhui</creator><creatorcontrib>Wang, Xianwen ; Fang, Zhichao ; Guo, Xinhui</creatorcontrib><description>Scholarly articles are discussed and shared on social media, which generates altmetrics. On the opposite side, what is the impact of social media on the dissemination of scholarly articles and how to measure it? What are the visiting patterns? Investigating these issues, the purpose of this study is to seek a solution to fill the research gap, specifically, to explore the dynamic visiting patterns directed by social media, and examine the effects of social buzz on the article visits. Using the unique real referral data of 110 scholarly articles, which are daily updated in a 90-day period, this paper proposes a novel method to make analysis. We find that visits from social media are fast to accumulate but decay rapidly. Twitter and Facebook are the two most important social referrals that directing people to scholarly articles, the two are about the same and account for over 95 % of the total social referral directed visits. There is synchronism between tweets and tweets resulted visits. Social media and open access are playing important roles in disseminating scholarly articles and promoting public understanding science, which are confirmed quantitatively for the first time with real data in this study.</description><identifier>ISSN: 0138-9130</identifier><identifier>EISSN: 1588-2861</identifier><identifier>DOI: 10.1007/s11192-016-2086-z</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Computer Science ; Decay rate ; Digital media ; Information Storage and Retrieval ; Library Science ; Social networks</subject><ispartof>Scientometrics, 2016-11, Vol.109 (2), p.1365-1376</ispartof><rights>Akadémiai Kiadó, Budapest, Hungary 2016</rights><rights>Copyright Springer Science & Business Media 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-d276b5d686c8b9b6420ef3de051ba658cbabb5469f55915563993afe0107cb543</citedby><cites>FETCH-LOGICAL-c359t-d276b5d686c8b9b6420ef3de051ba658cbabb5469f55915563993afe0107cb543</cites><orcidid>0000-0002-7236-9267</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11192-016-2086-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11192-016-2086-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Wang, Xianwen</creatorcontrib><creatorcontrib>Fang, Zhichao</creatorcontrib><creatorcontrib>Guo, Xinhui</creatorcontrib><title>Tracking the digital footprints to scholarly articles from social media</title><title>Scientometrics</title><addtitle>Scientometrics</addtitle><description>Scholarly articles are discussed and shared on social media, which generates altmetrics. On the opposite side, what is the impact of social media on the dissemination of scholarly articles and how to measure it? What are the visiting patterns? Investigating these issues, the purpose of this study is to seek a solution to fill the research gap, specifically, to explore the dynamic visiting patterns directed by social media, and examine the effects of social buzz on the article visits. Using the unique real referral data of 110 scholarly articles, which are daily updated in a 90-day period, this paper proposes a novel method to make analysis. We find that visits from social media are fast to accumulate but decay rapidly. Twitter and Facebook are the two most important social referrals that directing people to scholarly articles, the two are about the same and account for over 95 % of the total social referral directed visits. There is synchronism between tweets and tweets resulted visits. Social media and open access are playing important roles in disseminating scholarly articles and promoting public understanding science, which are confirmed quantitatively for the first time with real data in this study.</description><subject>Computer Science</subject><subject>Decay rate</subject><subject>Digital media</subject><subject>Information Storage and Retrieval</subject><subject>Library Science</subject><subject>Social networks</subject><issn>0138-9130</issn><issn>1588-2861</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp1kMFOAjEQhhujiYg-gLcmnqszW1raoyGKJiRe8Nx0u11YXCi25QBPb8l68OJpDvN__2Q-Qu4RHhFg-pQQUVcMULIKlGSnCzJCoRSrlMRLMgLkimnkcE1uUtpAYTioEZkvo3Vf3W5F89rTplt12fa0DSHvY7fLieZAk1uH3sb-SG3Mnet9om0MW5qC60p465vO3pKr1vbJ3_3OMfl8fVnO3tjiY_4-e14wx4XOrKmmshaNVNKpWtdyUoFveeNBYG2lUK62dS0mUrdCaBRCcq25bT0gTF1Z8DF5GHr3MXwffMpmEw5xV04aVAqU4ILLksIh5WJIKfrWlG-2Nh4Ngjn7MoMvU3yZsy9zKkw1MOn8-crHP83_Qj8DXW4A</recordid><startdate>20161101</startdate><enddate>20161101</enddate><creator>Wang, Xianwen</creator><creator>Fang, Zhichao</creator><creator>Guo, Xinhui</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>E3H</scope><scope>F2A</scope><orcidid>https://orcid.org/0000-0002-7236-9267</orcidid></search><sort><creationdate>20161101</creationdate><title>Tracking the digital footprints to scholarly articles from social media</title><author>Wang, Xianwen ; Fang, Zhichao ; Guo, Xinhui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-d276b5d686c8b9b6420ef3de051ba658cbabb5469f55915563993afe0107cb543</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Computer Science</topic><topic>Decay rate</topic><topic>Digital media</topic><topic>Information Storage and Retrieval</topic><topic>Library Science</topic><topic>Social networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Xianwen</creatorcontrib><creatorcontrib>Fang, Zhichao</creatorcontrib><creatorcontrib>Guo, Xinhui</creatorcontrib><collection>CrossRef</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><jtitle>Scientometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Xianwen</au><au>Fang, Zhichao</au><au>Guo, Xinhui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tracking the digital footprints to scholarly articles from social media</atitle><jtitle>Scientometrics</jtitle><stitle>Scientometrics</stitle><date>2016-11-01</date><risdate>2016</risdate><volume>109</volume><issue>2</issue><spage>1365</spage><epage>1376</epage><pages>1365-1376</pages><issn>0138-9130</issn><eissn>1588-2861</eissn><abstract>Scholarly articles are discussed and shared on social media, which generates altmetrics. On the opposite side, what is the impact of social media on the dissemination of scholarly articles and how to measure it? What are the visiting patterns? Investigating these issues, the purpose of this study is to seek a solution to fill the research gap, specifically, to explore the dynamic visiting patterns directed by social media, and examine the effects of social buzz on the article visits. Using the unique real referral data of 110 scholarly articles, which are daily updated in a 90-day period, this paper proposes a novel method to make analysis. We find that visits from social media are fast to accumulate but decay rapidly. Twitter and Facebook are the two most important social referrals that directing people to scholarly articles, the two are about the same and account for over 95 % of the total social referral directed visits. There is synchronism between tweets and tweets resulted visits. Social media and open access are playing important roles in disseminating scholarly articles and promoting public understanding science, which are confirmed quantitatively for the first time with real data in this study.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11192-016-2086-z</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-7236-9267</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0138-9130 |
ispartof | Scientometrics, 2016-11, Vol.109 (2), p.1365-1376 |
issn | 0138-9130 1588-2861 |
language | eng |
recordid | cdi_proquest_journals_1880853536 |
source | SpringerLink Journals - AutoHoldings |
subjects | Computer Science Decay rate Digital media Information Storage and Retrieval Library Science Social networks |
title | Tracking the digital footprints to scholarly articles from social media |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T06%3A05%3A17IST&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=Tracking%20the%20digital%20footprints%20to%20scholarly%20articles%20from%20social%20media&rft.jtitle=Scientometrics&rft.au=Wang,%20Xianwen&rft.date=2016-11-01&rft.volume=109&rft.issue=2&rft.spage=1365&rft.epage=1376&rft.pages=1365-1376&rft.issn=0138-9130&rft.eissn=1588-2861&rft_id=info:doi/10.1007/s11192-016-2086-z&rft_dat=%3Cproquest_cross%3E1880853536%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1880853536&rft_id=info:pmid/&rfr_iscdi=true |