Driving Mechanisms of User Engagement With AI-Generated Content on Social Media Platforms: A Multimethod Analysis Combining LDA and fsQCA

Driving Mechanisms of User Engagement With AI-Generated Content on Social Media Platforms: A Multimethod Analysis Combining LDA and fsQCA

With the rapid development of artificial intelligence (AI) technologies, AI-generated content (AIGC) on social media platforms has significantly increased. This study collected text data related to AIGC from mainstream social media platforms and employed the Latent Dirichlet Allocation (LDA) topic m...

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Bibliographic Details
Main Authors: Jiajun Hou, Hongju Lu, Baojun Wang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Artificial intelligence-generated content (AIGC)
social media platforms
user engagement
LDA model
configurational analysis
Online Access:https://ieeexplore.ieee.org/document/11080437/
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Summary:With the rapid development of artificial intelligence (AI) technologies, AI-generated content (AIGC) on social media platforms has significantly increased. This study collected text data related to AIGC from mainstream social media platforms and employed the Latent Dirichlet Allocation (LDA) topic model to uncover the thematic characteristics of AIGC. The analysis was further integrated with the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT) to identify seven key conditional variables: the maturity of AIGC technology, users’ perception of the authenticity of AIGC, users’ perception of the usefulness of AIGC, users’ perception of the entertainment value of AIGC, the commercialization level of AIGC, the personalization level of AIGC recommendations on the platform, and the ecosystem management and interaction atmosphere of AIGC on the platform. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), this study identified seven configurational paths that drive user engagement with AIGC on social media platforms, which were ultimately summarized into three core pathways: user perception—platform recommendation pathway, user perception—platform atmosphere pathway, and technology characteristics—user perception—platform recommendation—platform atmosphere pathway. The results indicate that users’ perceptions of the usefulness of AIGC are a key factor in driving user engagement with AIGC on social media platforms.
ISSN:2169-3536

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