Authenticity across human and synthetic influencers: a configurational review of source evaluation and social influence
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Published on: March 3, 2026
Xiao-Rui Lin1,2, Amily Fikry2, Wei-Guo Zhang3
1School of Business, Shanxi Datong University, Shanxi, China.
Introduction:
Authenticity has become a central concern in social media influencer research, yet its construction is examined differently across human, virtual, and artificial intelligence (AI)-mediated sources. This study examines how authenticity is constructed and evaluated across these influencer contexts.
Methods:
This study conducted a corpus-based review of 1,676 English-language research articles indexed in the Web of Science Core Collection and Scopus between July 2017 and March 2026. BERTopic modeling was employed to identify recurring semantic topics, followed by an abductive, theory-informed synthesis organized around source ontology and the dominant basis of authenticity construction. Temporal changes in relative topic prevalence were examined across the eight complete calendar years from 2018 to 2025.
Results:
The synthesis identified four configurations: relational human authenticity, curated human authenticity, humanized synthetic authenticity, and governed synthetic authenticity. Human-influencer research emphasizes biography, accumulated interaction, commercial congruence, and motive transparency, whereas synthetic-influencer research places greater weight on persona coherence, humanization, identity disclosure, ownership, automation, and accountability. Credibility, trust, parasocial interaction, social presence, and engagement emerge as closely associated evaluative processes. Temporal analysis revealed selective changes in relative topic prevalence, particularly concerning virtual-influencer credibility, humanization, and tourism-related applications, while most topics showed no clear linear trend.
Discussion:
The findings provide a corpus- and method-dependent framework for understanding how authenticity is constructed and evaluated across human and synthetic influencer contexts. The framework highlights distinct evidential foundations underlying authenticity judgments and offers a basis for further research on source evaluation and social influence.
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