How Users' Personality Traits Predict Sentiment Tendencies of User‐Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning.
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| Title: | How Users' Personality Traits Predict Sentiment Tendencies of User‐Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning. |
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| Authors: | Yang, Yongqing (AUTHOR), Xu, Jianyue (AUTHOR), Zhao, Ling (AUTHOR), Land, Lesley Pek Wee (AUTHOR), Li, Wenli (AUTHOR) |
| Source: | Journal of Personality. Oct2025, Vol. 93 Issue 5, p1175-1188. 14p. |
| Subjects: | Personality, User-generated content, Twitter (Web resource), Mood (Psychology), Pattern perception, Social media, Public opinion, Machine learning |
| Abstract: | Objective: Social media content created by users with different personality traits presents various sentiment tendencies, easily leading to irrational public opinion. This study aims to explore the relationships between users' personality traits and sentiment tendencies of user‐generated content (UGC). Method: We crawled 18,686 tweets of 1, 215 users from Twitter to figure out the relationships between personality traits and sentiment tendencies. This study utilizes Essays and Sentiment datasets to train machine learning models for the identification of personality traits and sentiment tendencies and then explores the configuration effect of personality traits on sentiment tendency via crisp‐set Qualitative Comparative Analysis (csQCA). Result: The findings suggest that (1) one‐dimensional personality trait is not a necessary condition for the sentiment tendencies of UGC. (2) There are multiple equivalent configurations that lead to the sentiment tendencies of UGC. Conclusion: The study suggests that the sentiment tendencies pattern of UGC can be discovered via the configurations of various dimensions of personality traits. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Personality is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 187860174 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: How Users' Personality Traits Predict Sentiment Tendencies of User‐Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Yongqing%22">Yang, Yongqing</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Jianyue%22">Xu, Jianyue</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Ling%22">Zhao, Ling</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Land%2C+Lesley+Pek+Wee%22">Land, Lesley Pek Wee</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Wenli%22">Li, Wenli</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Personality%22">Journal of Personality</searchLink>. Oct2025, Vol. 93 Issue 5, p1175-1188. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Personality%22">Personality</searchLink><br /><searchLink fieldCode="DE" term="%22User-generated+content%22">User-generated content</searchLink><br /><searchLink fieldCode="DE" term="%22Twitter+%28Web+resource%29%22">Twitter (Web resource)</searchLink><br /><searchLink fieldCode="DE" term="%22Mood+%28Psychology%29%22">Mood (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+perception%22">Pattern perception</searchLink><br /><searchLink fieldCode="DE" term="%22Social+media%22">Social media</searchLink><br /><searchLink fieldCode="DE" term="%22Public+opinion%22">Public opinion</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objective: Social media content created by users with different personality traits presents various sentiment tendencies, easily leading to irrational public opinion. This study aims to explore the relationships between users' personality traits and sentiment tendencies of user‐generated content (UGC). Method: We crawled 18,686 tweets of 1, 215 users from Twitter to figure out the relationships between personality traits and sentiment tendencies. This study utilizes Essays and Sentiment datasets to train machine learning models for the identification of personality traits and sentiment tendencies and then explores the configuration effect of personality traits on sentiment tendency via crisp‐set Qualitative Comparative Analysis (csQCA). Result: The findings suggest that (1) one‐dimensional personality trait is not a necessary condition for the sentiment tendencies of UGC. (2) There are multiple equivalent configurations that lead to the sentiment tendencies of UGC. Conclusion: The study suggests that the sentiment tendencies pattern of UGC can be discovered via the configurations of various dimensions of personality traits. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Personality is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=187860174 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/jopy.13000 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1175 Subjects: – SubjectFull: Personality Type: general – SubjectFull: User-generated content Type: general – SubjectFull: Twitter (Web resource) Type: general – SubjectFull: Mood (Psychology) Type: general – SubjectFull: Pattern perception Type: general – SubjectFull: Social media Type: general – SubjectFull: Public opinion Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: How Users' Personality Traits Predict Sentiment Tendencies of User‐Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Yongqing – PersonEntity: Name: NameFull: Xu, Jianyue – PersonEntity: Name: NameFull: Zhao, Ling – PersonEntity: Name: NameFull: Land, Lesley Pek Wee – PersonEntity: Name: NameFull: Li, Wenli IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00223506 Numbering: – Type: volume Value: 93 – Type: issue Value: 5 Titles: – TitleFull: Journal of Personality Type: main |
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