The mechanism of generative AI’s construction of cultural identity: an empirical study based on Generation Z’s social media behavior

Generative AI (AIGC) is fundamentally reshaping the landscape of digital content, yet its specific predictive relationship with the cultural identity of “digital natives” (Generation Z) remains underexplored compared to traditional User-Generated Content (UGC). While AIGC offers efficiency and aesthetic novelty, it lacks the inherent “human touch” of UGC. This study investigates this divergence by analyzing 11,628 comments from the Douyin platform. We employed a mixed-method approach combining semantic network analysis and structural equation modeling (SEM) to compare user responses across emotional, technological, and social dimensions. Our findings reveal a distinct “two-path” mechanism: while UGC fosters identity through a “Warm Path” of emotional resonance, AIGC is structurally associated with a significantly more robust identity profile via a “Cool Path” predicted by technical curiosity and exclusive topic circling. Crucially, we identify an “Authenticity Paradox”: the lack of traditional human touch in AIGC does not alienate Generation Z; rather, the resulting “mindful friction”—quantified by a significant negative path effect between sentiment and identity <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"> <mml:mo stretchy="true">(</mml:mo> <mml:mi>β</mml:mi> <mml:mo>=</mml:mo> <mml:mo>−</mml:mo> <mml:mn>0.370</mml:mn> <mml:mo stretchy="true">)</mml:mo> </mml:math> —functions as a subcultural filter.