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dc.contributor.authorHassan, SU
dc.contributor.authorAljohani, Naif Radi
dc.contributor.authorIdrees, N
dc.contributor.authorSarwar, R
dc.contributor.authorNawaz, R
dc.contributor.authorMartínez-Cámara, E
dc.contributor.authorVentura, S
dc.contributor.authorHerrera, F
dc.date.accessioned2020-10-09T10:56:39Z
dc.date.available2020-10-09T10:56:39Z
dc.date.issued2019-12-14
dc.identifier.citationHassa, S.U., Aljohani, N.R., Idrees, N., Sarwar, R., Nawaz, R., Martínez-Cámara, E., Ventura, S. and Herrera, F. (2020) Predicting literature's early impact with sentiment analysis in Twitter, Knowledge-Based Systems, 192, 105383.en
dc.identifier.issn0950-7051en
dc.identifier.doi10.1016/j.knosys.2019.105383en
dc.identifier.urihttp://hdl.handle.net/2436/623701
dc.descriptionThis is an accepted manuscript of an article published by Elsevier in Knowledge-Based Systems on 14/12/2019, available online: https://doi.org/10.1016/j.knosys.2019.105383 The accepted version of the publication may differ from the final published version.en
dc.description.abstract© 2019 Elsevier B.V. Traditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. ‘number of unique Twitter users’, improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literature's early impact.en
dc.formatapplication/pdfen
dc.languageen
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttps://www.sciencedirect.com/science/article/abs/pii/S095070511930629X?via%3Dihuben
dc.subjectaltmetricsen
dc.subjectTwitteren
dc.subjectsentiment analysisen
dc.subjectuser categoryen
dc.subjectpredicting citationsen
dc.titlePredicting literature's early impact with sentiment analysis in Twitteren
dc.typeJournal articleen
dc.identifier.journalKnowledge-Based Systemsen
dc.date.updated2020-10-07T17:59:17Z
dc.identifier.articlenumber105383
dc.date.accepted2019-12-11
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW09102020RSen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2021-12-14en
dc.source.volume192
dc.description.versionPublished version
refterms.dateFCD2020-10-09T10:53:34Z
refterms.versionFCDAM


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