Citation
Li, Mengxin and Bolong, Jusang Bin
(2025)
Research on user social platform selection preferences in the Big Five personality dimension.
Journal of Humanities, Arts and Social Science, 9 (7).
pp. 1259-1263.
ISSN 2576-0556; eISSN: 2576-0548
Abstract
This study, based on the Big Five Personality Theory, explores the influence of
personality traits on the choice of social platforms. Existing research indicates that
there is a correlation between personality and online behavior, but studies on the
characteristics of different platforms are still insufficient. The research adopted the
questionnaire survey method, using the revised NEO-FFI scale and the platform
usage preference questionnaire. A total of 1024 valid samples were collected
through online and offline channels, covering different age and occupational
groups. Data analysis reveals that users with a higher level of extroversion tend to
prefer instant messaging platforms such as WeChat and QQ. The trait of openness
significantly influences users’ choices of content creation platforms like
Xiaohongshu and Bilibili. The neurotic dimension is positively correlated with the
frequency of social platform usage. Agreeableness traits promote interactive behaviors such as likes and comments, while users with high conscientiousness have a
shorter usage duration. Age plays a moderating role in the relationship between
personality traits and platform choice. For instance, the openness of the younger
group is more strongly associated with the use of innovative platform functions.
This study innovatively constructed a personal-platform selection model, revealing
the functional requirements of users with different traits for social platforms. The
results have practical value for the personalized service design of the platform, such
as the development of stress-relieving functions for neurotic users, or providing
content creation tools for highly open-minded users. This research fills the gap in
platform-specific studies, and in the future, longitudinal tracking can be combined
to further verify the validity of the model.
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