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Please use this identifier to cite or link to this item: https://digital.lib.ueh.edu.vn/handle/UEH/65306
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dc.contributor.authorVu M. Ngo-
dc.contributor.otherToan L. D. Huynh-
dc.contributor.otherPhuc V. Nguyen-
dc.contributor.otherNguyen Huu Huan-
dc.date.accessioned2022-10-27T02:34:07Z-
dc.date.available2022-10-27T02:34:07Z-
dc.date.issued2022-
dc.identifier.issn0036-9292 (Print), 1467-9485 (Online)-
dc.identifier.urihttps://digital.lib.ueh.edu.vn/handle/UEH/65306-
dc.description.abstractThis paper introduces novel data on public sentiment towards economic sanctions based on nearly 1 million social media posts in 108 countries during the Russia–Ukraine war by using machine learning. We show the geographical heterogeneity between government stances and public sentiment. Finally, we show how political regimes, trading relationships and political instability can predict how people perceive this war.en
dc.formatPortable Document Format (PDF)-
dc.publisherWiley-
dc.relation.ispartofScottish Journal of Political Economy-
dc.rightsJohn Wiley & Sons, Inc.-
dc.subjectPublic sentimenten
dc.subjectEconomic sanctionsen
dc.subjectRussia–Ukraine waren
dc.titlePublic sentiment towards economic sanctions in the Russia-Ukraine waren
dc.typeJournal Articleen
dc.identifier.doihttps://doi.org/10.1111/sjpe.12331-
ueh.JournalRankingISI-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.fulltextOnly abstracts-
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