Please use this identifier to cite or link to this item:
https://digital.lib.ueh.edu.vn/handle/UEH/65306
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Vu M. Ngo | - |
dc.contributor.other | Toan L. D. Huynh | - |
dc.contributor.other | Phuc V. Nguyen | - |
dc.contributor.other | Nguyen Huu Huan | - |
dc.date.accessioned | 2022-10-27T02:34:07Z | - |
dc.date.available | 2022-10-27T02:34:07Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 0036-9292 (Print), 1467-9485 (Online) | - |
dc.identifier.uri | https://digital.lib.ueh.edu.vn/handle/UEH/65306 | - |
dc.description.abstract | This 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.format | Portable Document Format (PDF) | - |
dc.publisher | Wiley | - |
dc.relation.ispartof | Scottish Journal of Political Economy | - |
dc.rights | John Wiley & Sons, Inc. | - |
dc.subject | Public sentiment | en |
dc.subject | Economic sanctions | en |
dc.subject | Russia–Ukraine war | en |
dc.title | Public sentiment towards economic sanctions in the Russia-Ukraine war | en |
dc.type | Journal Article | en |
dc.identifier.doi | https://doi.org/10.1111/sjpe.12331 | - |
ueh.JournalRanking | ISI | - |
item.fulltext | Only abstracts | - |
item.openairetype | Journal Article | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | INTERNATIONAL PUBLICATIONS |
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