Personalized Emphasis Framing for Persuasive Message Generation

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dc.contributor.authorDing, Tao
dc.contributor.authorPan, Shimei
dc.date.accessioned2025-01-08T15:08:55Z
dc.date.available2025-01-08T15:08:55Z
dc.date.issued2016-11
dc.descriptionProceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, November, 2016
dc.description.abstractIn this paper, we present a study on personalized emphasis framing which can be used to tailor the content of a message to enhance its appeal to different individuals. With this framework, we directly model content selection decisions based on a set of psychologically-motivated domainindependent personal traits including personality (e.g., extraversion) and basic human values (e.g., self-transcendence). We also demonstrate how the analysis results can be used in automated personalized content selection for persuasive message generation.
dc.description.urihttps://aclanthology.org/D16-1150
dc.format.extent10 pages
dc.genreconference papers and proceedings
dc.identifierdoi:10.13016/m2x4mc-8bo4
dc.identifier.citationDing, Tao, and Shimei Pan. “Personalized Emphasis Framing for Persuasive Message Generation.” In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, edited by Jian Su, Kevin Duh, and Xavier Carreras, 1432–41. Austin, Texas: Association for Computational Linguistics, 2016. https://doi.org/10.18653/v1/D16-1150.
dc.identifier.urihttps://doi.org/10.18653/v1/D16-1150
dc.identifier.urihttp://hdl.handle.net/11603/37206
dc.language.isoen_US
dc.publisherACL
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsAttribution 4.0 International CC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titlePersonalized Emphasis Framing for Persuasive Message Generation
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-5989-8543

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