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RIP Emojis and Words to Contextualize Mourning on Twitter

dc.contributor.authorXu, Xinyuanen
dc.contributor.authorManrique, Rubenen
dc.contributor.authorPereira Nunes, Bernardoen
dc.date.accessioned2025-05-30T01:28:30Z
dc.date.available2025-05-30T01:28:30Z
dc.date.issued2021-08-30en
dc.description.abstractThis paper aims to investigate the use of emojis to contextualize mourning on Twitter. Specifically, we seek to determine (i) whether an emoji is sufficient to contextualize expressions of grief; (ii) which emojis most accurately represent mourning; (iii) whether only words are used to contextualize mourning; (iv) which words are used to characterize mourning in tweets; and, (v) if there are differences in the expression of mourning in different languages. For this, we use a multi-stage method to conduct a comprehensive analysis of the manifestations of grieving behavior on Twitter, and created machine learning models to classify expressions of mourning in tweets. The main contributions from this work are (1) a gold standard of manually annotated mourning tweets; (2) classification models produced using machine learning ensemble methods and BERT contextual embeddings; and, (3) an extensive analysis of our findings opening up opportunities for new research. The results of this paper reveal emojis alone are insufficient for identifying expressions of mourning in tweets, and the combination of both emojis and words is the most effective strategy for contextualizing mourning online-the models achieved the 84.8%-97% F1 score in all datasets. Although words alone are capable of characterizing mourning contexts correctly, the English vocabulary is limited, and the contribution of RIP-the abbreviation for "rest in peace''-is highly decisive. Our results have also shown that the most relevant emojis for this context were emotional ones, such as \includegraphics[width=1em]twitter_brokenheart.png, and emojis are used in a uniform fashion in both Spanish and English.en
dc.description.statusPeer-revieweden
dc.format.extent7en
dc.identifier.isbn9781450385510en
dc.identifier.scopus85114792539en
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85114792539&partnerID=8YFLogxKen
dc.identifier.urihttps://hdl.handle.net/1885/733754522
dc.language.isoenen
dc.publisherAssociation for Computing Machinery (ACM)en
dc.relation.ispartofHT 2021 - Proceedings of the 32nd ACM Conference on Hypertext and Social Mediaen
dc.relation.ispartofseries32nd ACM Conference on Hypertext and Social Media, HT 2021en
dc.relation.ispartofseriesHT 2021 - Proceedings of the 32nd ACM Conference on Hypertext and Social Mediaen
dc.rightsPublisher Copyright: © 2021 ACM.en
dc.subjectBERTen
dc.subjectclassification modelsen
dc.subjectemojisen
dc.subjectsocial media mourningen
dc.titleRIP Emojis and Words to Contextualize Mourning on Twitteren
dc.typeConference paperen
dspace.entity.typePublicationen
local.bibliographicCitation.lastpage263en
local.bibliographicCitation.startpage257en
local.contributor.affiliationXu, Xinyuan; School of Archaeology & Anthropology, Research School of Humanities & the Arts, ANU College of Arts & Social Sciences, The Australian National Universityen
local.contributor.affiliationManrique, Ruben; Universidad de los Andes Colombiaen
local.contributor.affiliationPereira Nunes, Bernardo; School of Computing, ANU College of Systems and Society, The Australian National Universityen
local.identifier.ariespublicationa383154xPUB22264en
local.identifier.doi10.1145/3465336.3475100en
local.identifier.pure30bdc36e-b987-45bc-8710-df3f21fdb20aen
local.identifier.urlhttps://www.scopus.com/pages/publications/85114792539en
local.type.statusPublisheden

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