Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83075
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dc.contributor.authorJothiramalingam, Keerthanaen
dc.contributor.authorSesagiri Raamkumar, Aravinden
dc.contributor.authorGanesan, Savithaen
dc.contributor.authorMuthu Kumaran, Selvaen
dc.contributor.authorErdt, Mojisolaen
dc.contributor.authorTheng, Yin-Lengen
dc.contributor.editorŽumer, Majaen
dc.contributor.editorDobreva, Milenaen
dc.contributor.editorHinze, Annikaen
dc.date.accessioned2019-01-07T08:43:41Zen
dc.date.accessioned2019-12-06T15:11:19Z-
dc.date.available2019-01-07T08:43:41Zen
dc.date.available2019-12-06T15:11:19Z-
dc.date.issued2018en
dc.identifier.citationSesagiri Raamkumar, A., Ganesan, S., Jothiramalingam, K., Muthu Kumaran, S., Erdt, M., & Theng, Y.-L. (2018). Investigating the characteristics and research impact of sentiments in tweets with links to computer science research papers. 20th International Conference on Asia-Pacific Digital Libraries (ICADL 2018). doi:10.1007/978-3-030-04257-8_7en
dc.identifier.urihttps://hdl.handle.net/10356/83075-
dc.description.abstractResearch papers are often shared in Twitter to facilitate better readership. Tweet counts are embedded in journal websites and academic databases, to emphasize the impact of papers in social media. However, more number of tweets per paper is doubted as an indicator of research quality. Hence, there is a need to look at the intrinsic factors in tweets. Sentiment is one of such factors. Earlier studies have shown that neutral sentiment is predominantly found in tweets with links to research papers. In this study, the main intention was to have a closer look at the non-neutral sentiments in tweets to understand whether there is some scope for using such tweets in measuring the interim quality of the associated research papers. Tweets of 53,831 computer science papers from the Microsoft Academic Graph (MAG) dataset were extracted for sentiment classification. The non-neutral sentiment keywords and the attributed aspects of the papers were manually identified. Findings indicate that although neutral sentiments are majorly found in tweets, the research impact of papers which had all three sentiments was better than papers which had only neutral sentiment, in terms of both bibliometrics and altmetrics. Implications for future studies are also discussed.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.format.extent12 p.en
dc.language.isoenen
dc.rights© 2018 Springer Nature Switzerland AG. All rights reserved. This paper was published in 20th International Conference on Asia-Pacific Digital Libraries (ICADL 2018) and is made available with permission of Springer Nature Switzerland AG.en
dc.subjectTwitteren
dc.subjectTweet sentimentsen
dc.subjectDRNTU::Social sciences::Communicationen
dc.titleInvestigating the characteristics and research impact of sentiments in tweets with links to computer science research papersen
dc.typeConference Paperen
dc.contributor.schoolWee Kim Wee School of Communication and Informationen
dc.contributor.conference20th International Conference on Asia-Pacific Digital Libraries (ICADL 2018)en
dc.contributor.researchCentre for Healthy and Sustainable Cities (CHESS)en
dc.identifier.doi10.1007/978-3-030-04257-8_7en
dc.description.versionAccepted versionen
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