Show item record

dc.contributor.authorHaustein, Stefanie
dc.contributor.authorBowman, Timothy D.
dc.contributor.authorHolmberg, Kim
dc.contributor.authorTsou, Andrew
dc.contributor.authorSugimoto, Cassidy R.
dc.contributor.authorLarivière, Vincent
dc.date.accessioned2020-04-20T17:35:13Z
dc.date.availableNO_RESTRICTIONfr
dc.date.available2020-04-20T17:35:13Z
dc.date.issued2015-05-05
dc.identifier.urihttp://hdl.handle.net/1866/23283
dc.subjectInformation disseminationfr
dc.subjectScientometricsfr
dc.subjectPreprintsfr
dc.titleTweets as impact indicators : examining the implications of automated “bot” accounts on Twitterfr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. Faculté des arts et des sciences. École de bibliothéconomie et des sciences de l'informationfr
dc.identifier.doi10.1002/asi.23456
dcterms.abstractThis brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific articles deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. Our results show that automated Twitter accounts create a considerable amount of tweets to scientific articles and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose distinguishing between different levels of engagement—that is, differentiating between tweeting only bibliographic information to discussing or commenting on the content of a scientific work.fr
dcterms.isPartOfurn:ISSN:2330-1635fr
dcterms.isPartOfurn:ISSN:2330-1643fr
dcterms.languageengfr
UdeM.ReferenceFournieParDeposantTweets as impact indicators: Examining the implications of automated “bot” accounts on Twitter Haustein, S., Bowman, T.D., Holmberg, K., Tsou, A., Sugimoto, C.R., Larivière, V. (2016). Tweets as impact indicators: Examining the implications of automated “bot” accounts on Twitter. Journal of the Association for Information Science and Technology, 67(1): 232–238.fr
UdeM.VersionRioxxVersion acceptée / Accepted Manuscriptfr
oaire.citationTitleJournal of the association for information science and technology
oaire.citationVolume67
oaire.citationIssue1
oaire.citationStartPage232
oaire.citationEndPage238


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show item record

This document disseminated on Papyrus is the exclusive property of the copyright holders and is protected by the Copyright Act (R.S.C. 1985, c. C-42). It may be used for fair dealing and non-commercial purposes, for private study or research, criticism and review as provided by law. For any other use, written authorization from the copyright holders is required.