1-s2.0-S1751157716301961-main.pdf

ContentslistsavailableatScienceDirect

Journal

of

Informetrics

jou rn al h om ep a ge : w w w . e l s e v i e r . c o m / l o c a t e / j o i

Regular

article

Global

science

discussed

in

local

altmetrics:

Weibo

and

its

comparison

with

Twitter

Houqiang

Yu

_,

_Shenmeng

_Xu

_,

_Tingting

_Xiao

_,

_Brad

_M.

_Hemminger

_,

Siluo

Yang

a_{SchoolofInformationManagement,WuhanUniversity,Wuhan430072,China} b_{SchoolofInformationandLibraryScience,UNCChapelHill,ChapelHill,27514,USA}

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received18July2016

Receivedinrevisedform27February2017 Accepted27February2017

Availableonline20March2017

Keywords: Altmetrics Weibo

Altmetricsindicators Scholarlycommunication Distribution

a

b

s

t

r

a

c

t

Localaltmetricsiscurrentlyanintegralpartofthealtmetricslandscape.Thispaperaimsto investigatethecharacteristicsofmicroblogaltmetricsoftheChinesemicroblogplatform, Weibo,toshedlightonculturaldifferencesanddrawattentiontolocalaltmetricsin devel-opingcountries.Theanalysisisbasedon4.4millionrecordsprovidedbyAltmetric.com. DatacollectedarefromMarch2014toJuly2015.ItisfoundthatWeibousersdiscussglobal science,moreactivelycomparedwithseveralinternationalaltmetricssources.Statistical resultsshowstrongevidenceoftheimmediacyadvantageofmetricsbasedonWeiboas wellasTwitterandthegeneralaltmetricsovercitations.DistributionofWeiboaltmetrics onthearticlelevel,sourcelevelanddisciplinelevelarehighlyskewed.Overall,compared withTwitter,Weiboaltmetricspresentsimilardistributions,withsomeminorvariations. TobetterunderstandhowandwhyWeibousersdiscussglobalscientificarticles,thetop weiboedarticles,sourcesanddisciplinesareidentifiedandfurtherexplored.Ourcontent analysisshowsthatthecommonmotivationofscientificweibosistodisseminateordiscuss thearticlesbecausetheyareinteresting,surprising,academicallyusefulorpractically use-ful.Conclusionofarticlesisthemostfrequentlymentionedelementinscientificweibos.In addition,differentfromTwitter,Weibousershaveapreferencefortraditionalprestigious journals.

©2017ElsevierLtd.Allrightsreserved.

1. Introduction

Aseriesofbehaviorsareconductedintheresearchprocess,includingbutnotlimitedtoinformationseeking,saving, reading,annotating,brainstorming,experimentation,dataanalysis,paperdraftingandciting.Thecitingbehaviorisrecorded bycitations,givingbirthtocitationanalysis;inthemoderndigitalera,manybehaviorsarerecordedbyonlinescholarly toolsandplatforms,layingthefoundationofaltmetrics.Altmetrics,bycapturingdigitaltracesofscientificproducts,aims toimprovescholarlycommunication,scientificevaluationandliteraturediscovery(Moed,2015).Altmetricsresearchis developingfromtheoretical(Priem,2013)andcriticaldebate(Qiu&Yu,2015)tomoreempirical(Wang,Guo&Zhang, 2015),experimental(Friedrich,Bowman,Stock,&Haustein,2015)andapplication-oriented(Das,2015)studies.Sofar,digital tracesonmanytoolsandplatformshavebeenstudied,includingTwitter(Haustein,Peters,Sugimoto,Thelwall,&Larivière, 2014c),Mendeley(Thelwall&Wilson,2015),ResearchBlogging(Shema,Bar-Ilan&Thelwall,2014),F1000(Mohammadi&

∗ Correspondingauthor.

E-mailaddress:[email protected](S.Xu).

Fig.1.Fourtypesofaltmetricsstudies.

Thelwall,2013),ResearchGate(Thelwall&Kousha,2015)andYouTube(Kousha,Thelwall&Abdoli,2012),amongothers. Whilescientificarticleisstillthemostcommonlystudiedtypeofscientificproduct,manyothertypesareexplored,for instance,blogs(Shema,Bar-Ilan&Thelwall,2012),software(ImpactStory,2016),slides(Kraker,Lex,Gorraiz,Gumpenberger, &Peters,2015),datasets(Petersetal.,2015),andvideos(Koushaetal.,2012).Altmetricshasbeenappliedinscientific evaluation.Evaluatedobjectscanbeajournal(Loach&Evans2015),aninstitution(Petersetal.,2014;Rehemtula,Rosa, Leitao,&Avilés,2014),adiscipline(Holmberg&Thelwall2014)orascientist(Kolahi,2015).Someinstitutions(ScienceOpen, 2016)haveenableduserstorankliteratureretrievalresultsbytheAltmetricAttentionScore.

Altmetricshasdrawnattentionfromworldwideacademia.AsshowninFig.1,fourtypesofaltmetricsresearchare definedbasedonthegeographical variationofaltmetricssourcesandscientific outputs.Type-Bandtype-D altmetrics studiesinvestigatethecommunicationbetweenlocalscienceandglobalscience,whiletype-Caltmetricsstudiesmainly focusondomesticscientificcommunication.Alperin(2013)arguesthatbydisseminatingresearchinonlinesocialmedia, thealtmetricsmovementwouldreducethebiascausedbyleadingbibliographicdatabaseswhereresearchfromdeveloping countriesisunderrepresented.However,altmetricsresearchhithertohasbeenfocusedontype-Astudieswheredataare collectedfrominternationalplatforms,ofresearchfrominternationalmainstreampublishers,prevailinglyindeveloped countries.AfewresearchershaveconsideredlocalaltmetricsandconductedType-Dstudies.Alperin(2015)investigated thecoverageofaltmetricsdataintheprominentLatinAmericanjournalportalSciELOandfoundthecoveragelevelofmost socialmediasourceswaszeroornegligible.Poplasen(Poplaˇsen&Zrni ´c2014)triedtousealtmetricsformeasuringscience inCroatia.Tammaro(2014)testedaltmetricsasanevaluationmethodforItalianscholarsinthehumanities.Torresetal. (Torres,Cabezas,&Jimenez,2013)conductedacasestudyonasampleofSpanishcommunicationstudies.However,these studiesalluseddatafrominternationalsocialmediaplatforms,neglectinglocalplatforms,whichplayanimportantrolein domesticscientificcommunication.

Type-Bandtype-Cstudiesareseldomseen,becausetheinfrastructureforanalyzinglocalaltmetrics,forexampledata aggregatingservices,isnotwellestablished.Thispaperarguesthatlocalaltmetricsisanintegralpartoftheholisticaltmetrics landscape.Discussionsonlocalaltmetricssourcescanfunctionaschannelsforintroducinginternationalscientificresearch. ThisistrueespeciallyincountrieswhereEnglishisnotthefirstlanguageorwhereglobalplatformsareblocked.Including localaltmetricsofdifferentcontextwouldrevealamorecomprehensiveviewofaresearchproduct’strueimpactoverthe world.

China,whilethrivingasastrongscientificpower,hasrestrictedaccesstomanyinternationalsocialmediaplatformssuch asTwitter,Facebook,andYouTube.SinaWeiboiscurrentlythemostwidelyusedmicroblogservice.Asatype-Bstudy,the aimofthisstudyistwofold:(1)Toinvestigatethecharacteristicsofanimportantlocalaltmetricssource,namelyWeibo,in discussingglobalscience.ItisofparticularinteresttoseehowglobalscientificresearchisdiscussedonWeibobyChinese users,consideringthatChinaisalargeeconomicentitywhichalsohasalargeacademiccommunity.(2)Tostudythe differencebetweenWeiboandTwitterasacomparisonoflocalaltmetricsandglobalaltmetrics.Althoughbothofthesetwo platformsarepopularmicroblogservices,theyhavecompletelydifferentusersfromdiverseculturalbackgrounds.Itisalso ofsignificantmeaningtocomparealtmetricsbasedonthesetwoplatformstorevealthenatureofaltmetricsofmicroblog servicesasawhole.

2. Researchquestions

ofWeiboaltmetricsonthearticle,sourceanddisciplinelevels?HowarethepatternscomparedwithTwitteraltmetrics?(4) HowandwhydoWeibousersdiscussglobalscientificarticles?Toanswerthisquestion,wemainlyfocusonthemotivation, elementsmentioned,andsentimentofthetopweibos.

Thisstudycontributestoaltmetricsstudiesinfourways:(1)Westudyanovelaltmetrics,Weibo,andcallforattention onlocalaltmetrics.Itwilladvancethedevelopmentofdomesticaltmetricsandtakealtmetricsstudiesonestepfurtherto provideamorecompleteviewofresearchimpact.(2)Wemeasuretheimmediacyofmicroblogaltmetricsinasystematic way.Theresultprovidesevidenceofwhetherandhowmuchmicroblogaltmetricsisfasterthancitations.(3)Weconducta comprehensivecomparisonbetweenWeiboandTwitter.Asthetwomostpopularmicroblogservices,thesimilaritiesand differencesbetweentheminglobalsciencediscussionareofparticularinteresttoscientificcommunication.(4)Moreover, inthisstudy,weusethelargestaltmetricsdatasetofWeiboandTwittersofar.Ourresultswillprovidereferencetoother relatedempiricalstudies.

3. Methodology

3.1. Usedterms

Forconvenienceofdiscussion,frequentlyusedtermsinthepaperareexplainedasfollows.

Altmetrics:Accordingtothecontext,thiscouldrefertotheresearchfieldorthealtmetricsindicators.

Weibo:ThepropernounWeiboreferstothemicroblogplatformSinaWeibo(Weibo.com).Thecommonnounweibo referstoamicroblogpostedonSinaWeibo,i.e.,aWeibopost.Whenusedasaverb,weiboreferstothebehaviorofposting amicroblogonWeibo.

Weiboaltmetrics:ThisdenotesaltmetricsbasedonSinaWeibo.WhenaresearchproductismentionedinaWeiboviaa link,aDOIorothertrackableways,wesaythatthisresearchproductreceivesaWeibomention,orhasWeiboaltmetrics.

Scientificweibo:Werefertoaweiboasascientificweibowhenitmentionsascientificproductandusuallycontainsa linktoit.

3.2. AbriefviewofWeibo

Weibo,originallytheChinesePinyinformicroblogandreferringtogeneralmicroblogservices,wasbrandedbySina Corporationin2010.NowWeibonormallyreferstoSinaWeibo.AsmicrocosmofChinesesociety,Weibohasattracteda widerangeofusers,includingcelebritiesandpublicfigures,organizationssuchasmediaoutlets,businesses,government agenciesandcharities,aswellasthegeneralpublic.Accordingtothelatestreport,inNovember2015,Weibohad261million monthlyactiveusers,comparedwith310millionmonthlyactiveTwitterusers.Weiboenablesuserstoexpressandshare theirideas,opinionsandstoriesintheformoftextandattachedmultimedia,includingimages,music,andshortvideos. Thereusedtobea140characterlimitonWeiboposts,however,thelimitwasremovedinFebruary2016.

Adominating97.2%ofWeibousersarefromChina,however,only21.9%ofTwitterusersarefromtheU.S.Inthissense, SinaWeiboisa localmicrobloggingserviceinChina.LikeTwitter,Weibo iswidelyusedfor scientificcommunication. Ithelpsevaluatethequantityandqualityofinformationfluxbetweentraditionalscientificexpertcommunitiesandthe broaderpublic(Zhao,Chen,Ge,Yu,&Shao,2014).OnereportshowsthatWeibouserswhoareengagedinscienceinclude professionalresearchers,teachersincollegesanduniversities,sciencewriters,journalists,andeditorsofscience,etc.(Liu, 2012).

3.3. Dataset

Altmetric.combegan totrackWeibo inMarch2014upon customers’request,and thetrackingended inJuly2015 (AltmetricLLP,2016),becausethecostbecameunaffordablewhenSinaWeibostoppeddistributingdatatoexternal com-paniesandeversincestartedupusingSocialgistastheirsolenon-Chinesedatabroker.1_{Altmetric.comprovidedthefull} datasetfromOctober2011toNovember2015forthisresearch,makingitpossibletoconductlargescalecomparisonand analysis.Thedatasetcontainedover4.4millionrecordsinJSONformat.Arelationaldatabasewasestablishedforretrieval’s convenience,andpythonscriptswereusedtoextractandanalyzethedata.

TocompareWeiboaltmetricswithotheraltmetricsinthesametime window,wefocusedonrecordscapturedby Altmetric.comfromMarch2014toJuly2015.Thetotalnumberofrecordswas1.99million.Forspecificresearchpurposes, forexampletheimmediacyanalysisofTwitter,recordsoftheentireperiod(i.e.October2011toNovember2015)were used.Inthedataset,eachrecordrepresentsaresearchproductofwhichaltmetricsactivitiesarecaptured.Thealtmetrics includethosebasedonWeibo,Twitter,Facebook,Blog,Wikipedia,News,GooglePlus,Policy,Reddit,F1000,PeerReviews, Video,andQ&A.AswillbedemonstratedinSection3.1,thesealtmetricshavevariouslevelsofcoverageonarticles.Abrief descriptionoftheseindicatorscanbefoundinTable1.

Table1

Briefdescriptionofaltmetricsindicators.

Indicator Description

Twitter NumberofmentionsanarticlereceivesonTwitter Weibo NumberofmentionsanarticlereceivesonWeibo Facebook NumberofmentionsanarticlereceivesonFacebookwalls

Blog Numberofmentionsanarticlereceivesonover9000academicandnon-academicblogsfeeds trackedviaRSSbyAltmetric.com

Wikipedia NumberofmentionsanarticlereceivesonWikipedia.org

News Numberofmentionsanarticlereceivesonover2000mainstreammedia GooglePlus NumberofmentionsanarticlereceivesonGooglePlus

Policy Mentionsofpublicationsinpolicydocuments.AccordingtoLiu,Konkiel,&Williams(2015), policydocumentsarefromdiversegroupssuchastheInternationalMonetaryFund,World HealthOrganization,andIntergovernmentalPanelonClimateChange.

Reddit NumberofmentionsanarticlereceivesonReddit

F1000 ArticlefactorcalculatedfromtherecommendationsforapublicationonF1000 PeerReviews Evaluationsofindividualoutputsfromcontributortoopenpost-publicationpeerreview

forumsPubpeerandPublons

Video NumberofmentionsanarticlereceivesinvideodescriptionsandcommentsonYouTube Q&A MentionofanarticleinquestionsandanswersonStackOverflow(stackoverflow.com)

3.4. Coveragecalculation

Coveragestatisticsreflectstheactivenessofaltmetricsindicators.Forexample,if90%ofasetofarticleshavebeentweeted butonly10%ofthemaresavedinMendeley,thenthecoverageofTwitteraltmetricsishigherthanMendeleyaltmetricsfor thissetofarticles.Thecoverageofaltmetricshavebeendiscussedinanumberofstudies.Forinstance,Araújo,Murakami, Lara,andFausto(2015)examinedTwitterandFacebookmentionsofarticlespublishedinaBrazilianLISjournal;Haustein, Bowman,Macaluso,Sugimoto,andLarivière(2014)exploredTwitteractivitiesandAltmetriccoverageofarticlesonArxiv; Zahedi,Costas,andWouters(2014)studiedthepresenceanddistributionofaltmetricsinthesetofpublications,acrossfields, anddocumenttypes;Hausteinetal.(2014b)investigatedtheadoptionofaltmetricssourcesbysampledbibliomatricians. While theseresultsprovidesomeideaabouttheactiveness ofaltmetrics,thecoveragestheyhaveexploredareunder differentcontexts,makingitdifficulttocomparethem.Ourstudy,therefore,calculates therelativecoverageofWeibo altmetrics,alongwithseveralotherpopularaltmetrics.Weusethetotalnumberofresearchrecords(1.99million)tracked byAltmetric.comintheWeibodatacollectionperiod(fromMarch2014toJuly2015)asthedenominator,andthenumber ofrecordsforeachaltmetricssourceasthenumerator,tocalculatethepercentage.Inthisway,despitethetotalcoverageof Altmetric.com,wecanhaveabetterunderstandingoftherelativecoverageofWeiboaltmetrics,comparedtoothers.

3.5. Immediacycalculation

ThetimedifferencebetweenthepublicationdateandthefirstWeibomentiondateiscalculatedtomeasuretheimmediacy ofWeiboaltmetrics.ThesamemeasuringtechniqueisusedonTwitterandthegeneralaltmetrics.AsdiscussedbyHaustein, Bowman,andCostas(2015),thefirstpubliconlineappearanceofVoR(VersionofRecord)shouldbeusedasthebasictime unittodeterminetheofficialpublicationdateofapaper,andamongmanyavailabledatasources,thepublicationdate collectedbyAltmetric.com,whichisamixtureofjournalissuedateandonlinedate,isoneofthebestproxiesforonline publication.Asaresult,Altmetricpublicationdatewasusedasthepublicationdateinourimmediacycalculation.LetTpw

denotethetimeTwbethefirstWeibomentiondate,TpbetheAltmetricpublicationdate,then

Tpw=Tw−Tp(days)

IfTpw<180days,theWeibomentionisdefinedtobeimmediate,comparedwithcitationsthatwouldtakeyearsto

accumulate(Brody&Harnad,2006).Theimmediacydistributionispresentedintimeintervalsof1day,7days(oneweek), 30days(onemonth),180days(halfyear),360days(oneyear)andover360days.WhenthefirstWeibomentionisprior totheAltmetricpublicationdate,Tpwisnegative.Forcomparison,theimmediacyofTwitterandthegeneralaltmetrics,

measuredrespectivelybyTptTpadefinedthesamewayasTpw,werealsocalculated.Taisthefirstdatewhenanarticleis

capturedbyanyaltmetricssourcetrackedbyAltmetric.com.

3.6. Distributioncalculationandclassificationschemafordisciplines

TostudythedistributionofWeiboaltmetrics,wecountthenumberofWeibopostsforeacharticle,eachsourceand eachdiscipline.Asobserved,Altmetric.comhasmaintainedthedisciplinecategorybasedontheScopusschema(Ss)andtwo

otherclassificationsystems,i.e.publisherdiscipline(Sp)andMedlinediscipline(Sm).48.2%oftheweiboedarticleshaveall

threeclassificationschemes,while78.7%ofarticleshaveatleastoneofthesethreeclassificationcode.Specifically,Sscovers

themostarticles(72.8%),Spcoversthesecondmostarticles(68.7%),andSmcoverstheleastarticles(59.5%).21.3%ofarticles

Table2

Motivationcodingscheme.

Firstlevelcode Secondlevelcode Definition

1Dissemination 1.1Dissemination−Interesting Tohighlighttheinterestingpart(s)ofthearticletoattractattention 1.2Dissemination−Surprising Tohighlightthesurprisingfact(s)beyondnormalexpectationtoattract

attention

1.3Dissemination−Academicallyuseful Tohighlighttheacademicusefulnessofthearticletoattractattention 1.4Dissemination−Practicallyuseful Tohighlightthepracticalusefulnessofthearticletoattractattention 1.5Dissemination−Linkonly Toprovidethelink

1.6Dissemination−Perfunctoryintroduction Toprovidethelinkwithverybrief,perfunctoryintroduction 1.7Dissemination−Requestforaccess Toaskforhelpwiththeaccessofthearticle

2Discussion 2.1Discussion−Interesting Toelaboratetheinterestingpart(s)ofthearticletoarouseinteractive communication

2.2Discussion−Surprising Toelaboratethesurprisingfact(s)ofthearticlebeyondnormalexpectationto arouseinteractivecommunication

2.3Discussion−Academicallyuseful Toelaboratetheacademicvalueofthearticletoarouseinteractive communication

2.4Discussion−Practicallyuseful Toelaboratethepracticalvalueofthearticletoarouseinteractive communication

2.5Discussion−Supportingaclaim Toreferencethearticletosupportaclaim 2.6Discussion−Criticizing Tocriticizethearticle

2.7Discussion−Responding Torespondtorelevantreportsofthearticle 3Marketing 3.1Marketing−Self-promotion Topromoteone’sownwork

3.2Marketing−Otherpromotion Topromoteothers’work 4Triggering 4.1Triggering−Reuse Tosaveforfuturereuse

4.2Triggering−Association Torelatethearticletorelevantideas

on,orfromothersmallscalepublishers,whomaintaintheirownclassificationsystemthatare,however,toosparsetobe recordedandanalyzed.

ConsideringthatSshasthehighestcoverage,hereinthisstudyweuseitinthedisciplinelevelanalysis.Itmustbe

noticedthatSsisslightly differentfromthecurrentScopusclassificationschema.ThecurrentScopusclassificationhas

divided“PhysicsandAstronomy”intotwosmallercategories,i.e.,“Astronomy,Astrophysics,SpaceScience”and“Physics”.Ss

has30disciplinesofwhichtheabbreviationandfulltitlecanbereferencedinAppendixA.PublisherSpmainlyconsistsof

theclassificationsystemsadoptedbytheERA(ExcellenceinResearchforAustralia),theNPG(NaturePublishingGroup), andSpringer.Specifically,ERAhas155disciplinesinitsclassificationsystem,Springerhas250,andNPGhas882,according totheAltmetric.comdata.AmidstweiboedarticlescoveredSp,ERAarticlestakeup76.6%,NPGarticlestakeup17.5%and

Springerarticlestakeup6.9%.TheNPG’sdisciplinesarefoundtobemorelikekeywordsratherthandisciplines.Duetothe heterogeneousnatureofSp;publisherdisciplinewasusedforreference.MedlinedisciplineSmprovidesdetaileddisciplines

ofgeneralmedicalscienceandhas96disciplines.Itcouldprovideinsightinthefurtheranalysisofdisciplinarydistribution ofthemedicalfield.Agoodcombinationofthesethreeclassificationsystemscanclearlyrevealthedisciplinarydistribution oftheweiboedarticles.

3.7. Contentanalysis

Inordertounderstandwhyusersweibothehighlyweiboedscholarlyarticles,contentanalysiswasconductedonweibos ofthetop1%(109of10,754)ofweiboedarticles,whichaccountsfor29%(10,775of37,200)ofthetotalweibos.Giventhat repostsdonotdiscloseenoughinformationfortheanalysisofmotivation,inthisstudywefocusonoriginalweibos,which occupy41%ofthetotal.Foreachofthe109highlyweiboedarticles,5originalweiboswererandomlyselected.Ifforan articlethenumberoforiginalweibosislessthan5,alloriginalweibosofthisarticlewerecollected.Asaresult,321original weibosweresampledandanalyzed.

Inthefirststep,wereferencedthecodingschemeofNa’swork(Na,2015)whenanalyzingmotivationfortweeting scholarlyarticlestoformabasicideaofthemotivationcategories.50weiboswerethencoded bythreecoders.After discussion,theinitialcodingschemewasformed.Inthesecondstep,another50weiboswerecodedusingtheinitialscheme. Theagreementratewas72%.Threecodersdiscussedagain,withaparticularfocusonthecodingdisagreementandpotential newcategories.Theinitialschemewasthenrevisedtothefinalversionofcodingscheme.Inthethirdstep,twocoderscoded all321weibos.Theagreementratewas87%.Thedisagreedweiboswerecodedagainbythethirdcoder.

Mainlythreeaspectsofweibocontentwereanalyzed.ThecodingschemaarepresentedinTables2–4.Allweiboswere manuallycoded.

Table3

Elementcodingscheme.

Code Definition

1Title Titleofthearticle 2Abstract Abstractofthearticle 3Methodology Methodologyofthearticle 4Conclusion Conclusionofthearticle

5Concept Termsorotherconceptsinthearticle

6Fragment Apieceofcontentinthearticle(anumber,afigureorasentence) 7Topic Maintopicofthearticle

8Summarize Briefsummarizationofthearticle 9Overall Generalfeelingofthearticle

10Indirectmention Mentionsindirectsources(e.g.,areporttalkingaboutthearticle) 11Purelink Mentionsnoelementofthearticle

Table4

Sentimentcodingscheme.

Code Description

1Neutral Whenweiboshowsnoclearpositiveornegativeattitudetowardsthearticle 2Positive Whenweiboshowsaclearpositiveattitudetowardsthearticle

3Negative Whenweiboshowsaclearnegativeattitudetowardsthearticle

Table5

RelativecoverageofWeibocomparedwithotheraltmetricssources.

Altmetrics No. Percentage Altmetrics No. Percentage Altmetrics No. Percentage Twitter 1308015 65.7% GooglePlus 48965 2.5% PeerReviews 9188 0.5% Facebook 347256 17.5% Policy 17638 0.9% Video 8293 0.4% Blogs 123656 6.2% Reddit 16300 0.8% Q&A 2983 0.2% Wikipedia 122309 6.2% F1000 11354 0.6%

News 119759 6.0% Weibo 10754 0.5%

criticizingspecificaspectsofthearticle;Marketingiswhenanarticleispraisedandrecommendedexplicitly;Triggering iswhenanarticleremindstheWeibouserofsomethingrelevant.(SeeTable2.)

(2) Element.ElementmentionedintheWeiboiscodedtodemonstratewhichpartofarticlesattractsthemostWeibo attention.(SeeTable3.)

(3) Sentiment.Sentimentoftheweibosindicateswhethertheuserhasapositive,neutral,ornegativeattitudetowardsthe articlementioned.(SeeTable4.)

3.8. Limitation

Thisstudyconsidersonlyarticlesamongallavailabletypesofresearchproducts,andheavilyreliesontheaccuracyof datacollectionbyAltmetric.com.However,AltmetricLLPiscurrentlytheonlyWeibotracker,andhasthemostprofessional articletrackingexperience(Zahedi,Fenner&Costas,2014).Therefore,thedatasetisconsideredcompleteandvalidenough toexploretheresearchquestions.

WhencalculatingtheimmediacyofWeiboaltmetrics,Altmetricpublicationdateisusedastheproxyofthefirstpublic onlineappearanceofVoR,becausethereishardlyasystematicwayofcollectingtheidealpublicationdate.Thisbottleneck awaitsbettersolutionbymakingvariousdatesreportedbypublishersmoretransparentandstandardized(Hausteinetal., 2015).

4. Results

4.1. CoverageofWeiboaltmetrics

Fig.2.ImmediacydistributionofWeiboaltmetrics.

4.2. ImmediacyofWeiboaltmetrics

Altmetricswasproposedtobemoreimmediatethancitations,whichusuallytakemonthsoryearstoaccumulate(Priem, Taraborelli,Groth,&Neylon,2010).Toconfirmthis,wedefinedandcalculatedtheimmediacyofWeiboposts,aswellas Twitterandthegeneralaltmetrics.ResultsareshowninFig.2.RegardingWeibo,Fig.2-AshowsthatWeiboaltmetricsis moreimmediatecomparedwithcitations,inthat69%ofarticleswithWeiboattentionarecapturedwithin180days.It’s highlightedthat44%ofarticlesinthedatasetgettheirfirstWeibopostinnomorethan7days,andparticularly,7%ofthem haveWeibopostspriortotheirformalpublication.Still,27%ofthearticlesreceivefairlylagged(Tpw>360days)Weibo

attention,implyingthatWeibousersalsodiscussoldarticles.ThegeneralaltmetricsrespondmoreslowlythanWeibo.As Fig.2-Billustrates,46%ofgeneralaltmetricshappenmorethan360daysafterpublishing.Thisimpliesthataltmetricssources havedifferentlevelsofimmediacy,andsomeofthemmaynotbeasimmediateasexpected.

Next,wecomparedtheimmediacyofWeibowithTwitter.InFig.2-C,itcanbeseenthatTwittersharesasimilarimmediacy distributionwithWeibo,reflectedinthat64%oftweetedarticlesarefirstlytweetedwithin180days,37%ofthemreceivetheir firsttweetwithin7days,andparticularly,12%aretweetedbeforeformalpublishing.Fig.2-Distheimmediacydistribution ofTwitterinabroadertimespan,showingaslightdropintimeintervalof[0,1]and(1,7],andabitofrisein[180,360]and (360,).Nevertheless,theshapeofthecurveisbasicallythesamewithFig.2-C,demonstratingthatthedistributionisstable andreflectsthenatureofTwitteraltmetrics.

4.3. DistributionofWeiboaltmetricsonthearticlelevel

Fig.3.DistributionofWeibovs.Twitter.

Fig.4.DistributionofTwittervs.citation.

ofTwitteraltmetricsandcitation,usingScopuscitationdataandAltmetric.comdataoverthreeperiods(i.e.January2012, January2013andJanuary2014).Adetaileddescriptionofthedatasetcanreference(Yu,2016a).AsshowninFig.4,10%of journalarticlesaccumulateonlyaround42%oftotalcitations,and20%ofjournalarticlesaccountforabout61%ofoverall citations.Hence,theoverallskewnessofmicroblogaltmetricsishigherthancitations.

Fig.5.FrequencydistributionofWeibovs.Twitter.

Table6

Motivationcodingresult.

Code No. Percentage

1.1Dissemination−Interesting 18 5.6% 1.2Dissemination−Surprising 10 3.1% 1.3Dissemination−Academicallyuseful 11 3.4% 1.4Dissemination−Practicallyuseful 7 2.2% 1.5Dissemination−Linkonly 17 5.3% 1.6Dissemination−Perfunctoryintroduction 67 20.9% 1.7Dissemination−Requestforaccess 1 0.3%

1Overalldissemination 131 40.8%

2.1Discussion−Interesting 24 7.5% 2.2Discussion−Surprising 33 10.3% 2.3Discussion−Academicallyuseful 37 11.5% 2.4Discussion−Practicallyuseful 33 10.3% 2.5Discussion−Supportingaclaim 23 7.2% 2.6Discussion−Criticizing 3 0.9% 2.7Discussion−Responding 12 3.7%

2Overalldiscussion 165 51.4%

3.1Marketing−Self-promotion 3 0.9% 3.2Marketing−Otherpromotion 17 5.3%

3Overallmarketing 20 6.2%

4.1Triggering−Reuse 1 0.3%

4.2Triggering−Association 4 1.3%

4Overalltriggering 5 1.6%

4.4. ContentanalysisofWeiboaltmetrics

Resultsofthecontentanalysisofthetop1%ofweibosareshowninTables6,7and9.Inaddition,statisticalresultof howmotivationandelementco-occurisshowninTable8.

Table6showsthatthemostcommonmotivationforweiboinganarticleistodisseminatethisarticlebyprovidingbotha linkandsomeperfunctoryintroduction(code1.6,20.9%).Itisfollowedbytodiscussbyelaboratingtheacademicusefulness ofthearticle(code2.3,11.5%),todiscussbyelaboratingthepracticalusefulnessofthearticle(code2.4,10.3%)andtodiscuss byelaboratingthesurprisingpart(s)(code2.2,10.3%)orinterestingpart(s)(code2.1,7.5%)ofthearticle.Meanwhile,asmall percentageofmarketing(6.2%)andtriggering(1.6%)areobserved.Ingeneral,discussionisthemajormotivationcategory (51.4%).

Table7

Elementcodingresult.

Code NO. Percentage

1Title 19 5.9%

2Abstract 1 0.3%

3Methodology 18 5.6%

4Conclusion 73 23.1%

5Concept 1 0.3%

6Fragment 28 8.7%

7Topic 46 14.3%

8Summarize 17 5.3%

9Overall 9 2.8%

10Indirectmention 2 0.6%

11Purelink 106 33.0%

Table8

Distributionofmotivation-elementpair(NO.>3).

M. E. NO. Percentage M. E. NO. Percentage M. E. NO. Percentage 1.6 11 45 14.0% 2.3 8 9 2.8% 2.5 4 5 1.6%

2.4 4 25 7.8% 1.1 11 7 2.2% 2.3 3 4 1.3%

2.1 4 16 5.0% 2.3 7 7 2.2% 2.3 11 4 1.3%

2.2 6 15 4.7% 1.1 7 6 1.9% 2.1 7 4 1.3%

1.5 11 15 4.7% 2.5 11 6 1.9% 2.4 9 4 1.3%

1.6 1 14 4.4% 2.2 7 5 1.6% 1.2 7 4 1.3%

3.2 11 13 4.1% 2.5 3 5 1.6% 1.4 11 4 1.3%

2.2 4 11 3.4% 1.6 7 5 1.6% 1.3 3 4 1.3%

2.3 4 10 3.1% 1.2 6 5 1.6%

*M.ismotivation;E.iselement;NO.isthenumberofcombinationofM.andE.;Percentage.isthepercentageofthecombinationinallpossiblecombinations. Forthecodenumber,pleaserefertoTables6and7.

Table9

Sentimentcodingresult.

Sentiment NO. Percentage

1Neutral 272 84.7%

2Positive 43 13.4%

3Negative 6 1.9%

Table8showsthepatternofhowthemotivationcategoriesco-occurwiththeelementcategories.Itiscommontosee inweibosthatanarticlelinkwithperfunctoryintroductionco-occurwithnomentionofanyelementofthearticle(14%). Plentyofweibosdiscusstheconclusionsofthearticlebecausetheyarepracticallyuseful(7.8%)orinteresting(5%).Users alsomentionfragmentofcontentinthearticlebecausetheyfinditbeyondnormalexpectation(4.7%).

Table9showsthatmostofweibosareneutral(84.7%),certainpercentagearepositive(13.4%),andveryfewarenegative (1.9%).

4.5. DistributionofWeiboaltmetricsonthesourcelevel

Fig.6.SourcedistributionofWeibovs.Twitter.

Table10

Topweiboedsourcesvs.toptweetedsources(top15).

No. weiboedsource Nw No. Tweetedsource Nt

1 Nature 866 1 arXiv 127413

2 arXiv 495 2 PLoSONE 25678

3 Science 421 3 SSRN 16308

4 Cell 319 4 Nature 6169

5 PNAS 308 5 PNAS 5916

6 NatureCommunications 205 6 Science(AAAS)News 5914

7 PLoSONE 165 7 BritishMedicalJournal(ClinicalResearchEdition) 5561 8 CellReports 160 8 ScientificReports 4716 9 NewEnglandJournalofMedicine 156 9 Science 4133 10 SSRN 152 10 AngewandteChemie.InternationalEdition 3809

11 MolecularCell 113 11 TheLancet 3342

12 JournalofClinicalOncology 112 12 NatureCommunications 3101 13 ScienceTranslationalMedicine 107 13 Figshare 2998

14 Neuron 99 14 JAMA 2939

15 TheLancet 99 15 PhysicalReviewLetters 2844

*Sharedtoppostedsourcesaredenotedinbold.

mentionedby10,606Weiboposts,while37,986sourceswerementionedby3.16milliontweets.Thenumberofboththe sourcesandpostsofWeiboweredwarfedbythoseofTwitter.

Fig.7.Overlappedjournalsofweiboed&tweetedjournals.

Shen,2011).AccordingtoTable10,CellgainshighattentiononWeibo,butnotequalattentiononTwitter,beingranked87th amongallthehighlytweetedjournals.OtherexamplesareScienceTranslationalMedicineandNeuron,whicharebothhighly mentionedonWeibobutmuchlessvisibleonTwitter.Ingeneral,multidisciplinaryjournalsandmedicalsciencesources getthemostattentiononWeibo.Twitteruserspaymuchattentiontochemicalsciencesources,forexample,Angewandte ChemieInternationalEdition(aGermanchemicaljournal)andJAMA.

Inaddition,aSpearman’srank-ordercorrelationwasruntodeterminetherelationshipbetweenthelistsofweiboed and tweetedsources.Intotal, 2414sourcesarementioned onWeibo;37986sourcesarementioned onTwitter.2006 sourcesarementionedbybothWeiboandTwitter.Thereisastrong,positivecorrelationbetweenthesetwolistofsources (r_s(2006)=0.75,p=0.00).

Asobservedabove,journalsthatarebothhighlyweiboedandtweetedhavehighImpactFactors.Toexplorethe relation-shipbetweenImpactFactorandtheattentiononbothWeiboandTwitter,wefirstexaminedhowmuchcommonjournals WeiboandTwitteruserspayattentionto.Fig.7showsthatlargely,around40%ofjournalsarebothweiboedandtweeted, regardlessofthenumberoftopjournalswelookat.Particularly,themosttopweiboedandtweetedarticlesoverlapeven more.WethenexaminedwhetherweiboedortweetedjournalshavehigherImpactFactors.TheaverageJIF(JournalImpact Factor)percentilereportedinJCR(JournalCitationReports)2015,designedformeasuringImpactFactorofjournalsacross field,isusedforcomparison’spurpose.Fig.8showsthattheaverageJIFpercentilesofthetweetedjournalsareevenly distributed,withapproximately5%ofthetweetedjournalsfallingoneachlevelofaverageJIFpercentile.Comparedwith Twitter,weiboedjournalshavetheiraverageJIFpercentilesmoreprevailinglyfallingonthehigherlevels.Forexample, morethan15%ofweiboedjournalshaveaverageJIFpercentileshigherthan95%.ThisindicatesthatWeibouserspaymore attentiontohigherimpactjournals,butTwitterusersdonothaveapreferenceonhighimpactjournals.

4.6. DistributionofWeiboaltmetricsonthedisciplinelevel

Therearesignificantdisciplinary differencesregardingcitationindicators.Comparisonbetweencitations ofphysics publicationsandthatofpsychologyones,forexample,canthusbemisleading.Weinvestigatethedisciplinarydifferencesof WeiboaltmetricsbasedonSs(seeMethodology,Section3.6).FromFig.9,weseethatthemostfrequentlyweiboedarticlesare

fromGeneraldisciplines(20.5%).ThismeansthatinterdisciplinarycontentgainsthemostattentionfromWeibo.Biochemistry, GeneticsandMolecularBiology(14.4%)isrankedthesecond.It’sfollowedbyHealthsciences(13.4%)andMedicine(13.2%), whichgetthethirdandfourthpositionrespectively.Thenextis“LifeSciences”,whichisrankedthefifth.Thesefivedisciplines arethemostfrequentlyweiboeddisciplines.Eachofthemhasapercentageover10%,andtheytogetheroccupy72.3%ofall theweiboedarticles.Thefollowingdisciplinesare“SocialSciences”(6.4%),“PhysicalSciences”(4.2%),“Neuroscience”(2.2%), “Psychology”(1.8%),“Economics,EconometricsandFinance”(1.7%),“AgriculturalandBiologicalSciences”(1.5%),and“Physics andAstronomy”(1.2%).These7disciplineshavepercentagesover1%andtogetheroccupy19.1%ofallweiboedarticles.The rest19disciplinesoccupytheremaining8.6%ofalltheweiboedarticles.

Fig.8. RelationshipbetweenImpactFactorandattentionfromWeibo/Twitter.

Fig.9. DisciplinedistributionofWeiboaltmetrics(basedonSs)..

discussextensivelyaboutHealthScienceandMedicineindicatesthatthesetwodisciplinesareofspecialinteresttousers ofmicroblogservices.Meanwhile,WeibouserspaythemostattentiontoGeneralandBiochemistry,GeneticsandMolecular Biology,showingChinesescientists’attentiontothesetwodisciplines.

WhilethedisciplinedistributionbasedonSsprovidesamacroview,thedistributionbasedonSpandSmprovidefurther

insightasshowninTable11.FromTable11,weseedisciplinedistributionsbasedonSpandSmarebasicallyinaccordance

withthatbasedonSs,butwithmoredetails.Forexample,undergeneral“BiologySciences”,itisfoundthat“Cellbiology”(8.6%, Sm#3),“Molecularbiology”(3.3%,Sm#6),and“Genetics”(1.1%,Sm#18)aremorementionedanddiscussedbyWeibousers.

Table11

Topweiboeddisciplines(basedonSpandSm).

N. Sp P. Sm P.

1 MedicalandHealthSciences 19.8% Science 28.7%

2 BiologicalSciences 11.5% Medicine 11.4%

3 Multidisciplinary 9.2% Cellbiology 8.6%

4 CognitiveScience 4.3% Neoplasms 5.3%

5 ClinicalSciences 4.0% Neurology 4.4%

6 Neurosciences 3.4% Molecularbiology 3.3% 7 OncologyandCarcinogenesis 3.3% Biology 2.4%

8 Psychology 3.1% Psychology 2.3%

9 PsychologyandCognitiveSciences 3.0% Allergyandimmunology 2.0% 10 PublicHealthandHealthServices 2.0% Biotechnology 1.6% 11 BiochemistryandCellBiology 1.8% Nutritionalsciences 1.6%

12 ChemicalSciences 1.7% Chemistry 1.5%

13 InformationandComputingSciences 1.5% Clinicallaboratorytechniques 1.4%

14 Immunology 1.2% Geneticsmedical 1.4%

15 PhysicalSciences 1.2% Endocrinology 1.3%

16 Engineering 1.2% Pediatrics 1.2%

17 Economics 1.0% Nanotechnology 1.1%

18 PoliticalScience 1.0% Genetics 1.1%

*Sp#1representsNo.1inSpcategory.

“SocialSciences”category,“CognitiveScience”(4.3%,Sp#4),“Psychology”(3.1%,Sp#8;2.3%,Sm#8),“Economics”(1%,Sp#17), and“PoliticalScience”(Sp#18)aremostfrequentlyweiboed.

5. Discussion

Thisisatype-Bstudy,whichpaysparticularattentiontohowinternationalresearchisdiscussedlocally.Thepapershows thattherelativecoverageofWeiboaltmetricsoverglobalscienceis0.5%.ThispercentageissimilartoPeerReview(0.5%), andhigherthanseveralglobalaltmetricssourceslikeVideo(0.4%)andQ&A(0.2%).However,itissignificantlydwarfedby Twitter(65.7%).Thisshowsdifferentlevelsofattentionbetweenalocalandaglobalmicroblogservice.Bycombiningour findingwiththepreviouslymeasuredabsolutecoverage,forexample,theTwittercoverageofPubMedarticles(Haustein etal.,2014c),theabsolutecoverageofWeibocanbeestimated.ChinesescholarshavebeencallingonestablishingChinese altmetricsinfrastructure.ThefullscholarlycommunicationactivenessofWeiboonbothdomesticandinternationalresearch shouldbemeasuredinthefuture.

Immediacycanbearelativeconceptaccordingtodifferentinterpretationsandresearchgoals.Comparedwiththecitation window,captureswithin180daysonWeibo areacceptabletobedefinedasimmediate.Thestudyshowsproofofthe immediacyofWeibo,Twitterandgeneralaltmetrics.69%ofweiboedarticlesreceivedtheirfirstweibowithin180daysof publication,44%werewithin7days,and7%werepriortoformalpublishing.Incontrast,64%oftweetedarticleshadtheir firsttweetwithin180days.

ThedistributionofWeiboaltmetricsis,comparedwithcitations,moreskewedonthearticlelevel,ismoreconcentrated onthesourcelevel,andpresentssignificantdifferencesonthedisciplinelevel.Specifically,20%ofweiboedarticlesare mentionedby70%oftheweibos.Theaveragenumberofweiboseacharticlereceivesis3.5,despitethefactthat84%ofthe weiboedarticlesreceivenomorethan3weibos.Twitterhasthissimilarpattern−20%oftweetedarticlesobtains77%of tweets.Eacharticlereceivesanaverageof6.2articles,and74%ofthetweetedarticleshadnomorethan3tweets.Thehigh skewnessindicatesthatWeiboaltmetricsisabletodistinguishfeaturedstudies.ThesourcedistributionofWeiboaltmetrics isconcentrated,evenmoresothanBradford’slawdistribution.ThisreflectsChina’sbiasedattentiontoprestigiousjournals, becauseChineseuniversitiesandinstitutionshaveunbalancedrewardsonpublicationsinthesejournals(Shao&Shen,2012; Hvistendahl,2013).Incontrast,Twitterusersdiscussmorediversesources,reflectedinthatFigshareisalsoamongthetop tweetedsources.Nevertheless,bothWeiboandTwitteruserspaymuchattentiontomedicalsources,whichindicatesthat medicalresearchisofsimilarlevelofinteresttoChinaandothernations.

6. Conclusion

Thestudy,basedonalarge-scaledataset,revealscharacteristicsofSinaWeiboasanimportantlocalaltmetricsplatform onwhichglobalscienceisdiscussed.WeconductedasystematiccomparisonbetweenWeiboandtheglobalmicroblogging platform,Twitter,aimingtodrawattentiontolocalaltmetricsindevelopingcountries,toinvestigatethecommonfeaturesof microblogaltmetrics,andtoshedlightonculturaldifferencesinscholarlycommunicationonthesesocialmediaplatforms. SinaWeibo,asalocalsocialmediaplatforminChina,ismeanwhiletheworld’ssecondlargestmicroblogservice.Weibo usersdiscussglobalsciencemoreactivelythanitisdiscussedonseveralglobalaltmetricssources.Beingachannelconnecting globalscienceandregionalcommunity,localsocialmediashouldbeconsideredasanintegralpartofthealtmetricslandscape. StrongevidenceshowsthatWeibo,aswellasTwitterandgeneralaltmetrics,havesignificantimmediacyadvantageover citations,endorsingearlierclaimsthataltmetricscanpotentiallydetectmorerealtimeimpactofresearchproducts.

ThehighlyskeweddistributionofWeiboandTwitteraltmetricsisnotsurprisingconsideringtheirnatureofsocialmedia (Banditwattanawong,Masdisornchote,&Uthayopas,2014;Jiang,Wang,Yang,&Li,2015).However,certaintypesofarticles, anumberofjournalsandspecificdisciplinesarefoundtoattractmoreWeiboattention.Thecommonmotivationsfor postingscientificweibosistodisseminateordiscussthearticlesbecausetheyareinteresting,surprising,academically useful,orpracticallyuseful.WeibouserspayparticularattentiononprestigiousjournalsbecauseoftheChineseacademic rewardsystem,whichhasbiasedawardsforpublicationsinthesejournals.Inaddition,discussionofarticlespublishedon importantopenaccessplatformsconfirmsChina’sacceptanceofnewformsofscientificcommunication.Whilefocusingthe mostonGeneraland“Biochemistry,GeneticsandMolecularBiology”,Weibousersshareinterestintopdiscusseddisciplines suchasHealthscience,MedicineandLifeScience,withTwitter.Thesedisciplineswithcomparativelyhighercoverageof altmetrics,aboveotherdisciplines,canmakeuseofsocialmediascholarlycommunicationandaltmetricstobetterachieve theirdevelopmentgoals.

Thisstudyconsidersonlyarticles.However,othertypesofresearchproductsalsomatter.Asaltmetricsstudiesevolve, researchershavenotedthatTwitteraccountsareofdifferentbackgrounds,forexample,automaticbots maintainedby individualsorinstitutions(Hausteinetal.,2016).Differentusercategoriesarealsoshowntohaveinfluenceonthevalueof tweets(Yu,2016b).Therefore,morecontentanalysistakingcontextinformationintoaccountisneeded,tofurtherreveal thenatureofWeiboandgeneralaltmetrics.

Acknowledgements

ThisresearchwassupportedbyChinaScholarshipCouncil(NO.201506270024)andsponsoredbyHumanitiesandSocial ScienceFoundationbytheMinistryofEducationofChina(16YJA870011)andtheResearchCenterofInformationTechnology &EconomicandSocialDevelopmentinZhejiangProvince.TheauthorsthankAltmetric.comforprovidingthedatasetand ProfessorJunpingQiuforhisinsightfulcomments.

AppendixA.

SeeTableA1.

TableA1

AbbreviationofScopusdisciplines

No. Abbv. FullName No. Abbv. FullName 1 AGRI AgriculturalandBiologicalSciences 16 HEALP HealthProfessions 2 ARTS ArtsandHumanities 17 HEALS HealthSciences

3 BIOC Biochemistry,GeneticsandMolecularBiology 18 IMMU ImmunologyandMicrobiology 4 BUSI Business,ManagementandAccounting 19 LIFES LifeSciences

5 CENG ChemicalEngineering 20 MATE MaterialsScience 6 CHEM Chemistry 21 MATH Mathematics 7 COMP ComputerScience 22 MEDI Medicine 8 DECI DecisionSciences 23 NEUR Neuroscience 9 DENT Dentistry 24 NURS Nursing

10 EART EarthandPlanetarySciences 25 PHAR Pharmacology,ToxicologyandPharmaceutics 11 ECON Economics,EconometricsandFinance 26 PHYSS PhysicalSciences

12 ENER Energy 27 PHYSA PhysicsandAstronomy 13 ENGI Engineering 28 PSYC Psychology

14 ENVI EnvironmentalScience 29 SOCI SocialSciences 15 GENE General 30 VETE Veterinary

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