How Users' Personality Traits Predict Sentiment Tendencies of User‐Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning.
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 (UG...
| Publicado en: | Journal of Personality Vol. 93; no. 5; pp. 1175 - 1189 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Wiley-Blackwell
Oct2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187860174&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187860174 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00223506 JPS jtl: Journal of Personality issn: 00223506 maglogo: Y pubinfo: dt: Oct2025 vid: 93 iid: 5 pid: 480 pub: Wiley-Blackwell artinfo: ui: 187860174 10.1111/jopy.13000 ppf: 1175 ppct: 14 formats: tig: atl: How Users' Personality Traits Predict Sentiment Tendencies of User‐Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning. aug: au: Yang, Yongqing Xu, Jianyue Zhao, Ling Land, Lesley Pek Wee Li, Wenli affil: Shenyang University of Technology, Shenyang, China Shandong Technology and Business University, Yantai, China Dalian University of Technology, Dalian, China Huazhong University of Science and Technology, Wuhan, China The University of New South Wales, Sydney New South Wales,, Australia su: Twitter (Web resource) Personality Mood (Psychology) Social media Public opinion User-generated content Pattern perception Machine learning sug: subj: Personality Mood (Psychology) Social media Public opinion User-generated content Pattern perception Machine learning Twitter (Web resource) keyword: csQCA machine learning personality traits sentiment tendencies social media user‐generated content csQCA machine learning personality traits sentiment tendencies social media user‐generated content ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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