How Users' Personality Traits Predict Sentiment Tendencies of User-Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning.
Yongqing Yang, Jianyue Xu, Ling Zhao, Lesley Pek Wee Land, Wenli Li
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引用次数: 0
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.
期刊介绍：
Journal of Personality publishes scientific investigations in the field of personality. It focuses particularly on personality and behavior dynamics, personality development, and individual differences in the cognitive, affective, and interpersonal domains. The journal reflects and stimulates interest in the growth of new theoretical and methodological approaches in personality psychology.
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