Emotional and behavioral asymmetries in user responses to virtual versus human influencers

As AI virtual influencers (AIVIs) proliferate, understanding human-AI emotional engagement is vital for advancing affective computing. To examine whether AIVIs evoke comparable affective responses to human influencers (HIs), we analyzed 156,918 Instagram comments that received 12+ million likes, integratinglarge language model (LLM)-derived knowledge graphs, emotion analysis, and machine learning. HI-directed discourse shows more differentiated emotional expression and a pattern consistent with more cohesive semantic networks than AIVI-directed discourse.

AIVI-directed comments are less likely to express positive affect (24.8% vs. 41.5%) and also less likely to express negative affect (5.8% vs. 9.3%), indicating reduced affective expression of either valence rather than heightened negativity. This group-level tendency is not uniform: affective engagement varies substantially across AIVIs, and that variation is more closely associated with positive than with negative expression. Our Multi-Signal Embedding Network (MSEN), incorporating behavioral metrics and OpenAI semantic embeddings, classified influencer types from behavioral, affective and semantic features (F1 = 0.89), outperforming baselines including a state-of-the-art zero-shot LLM.

These findings identify a boundary condition for the Computers Are Social Actors (CASA) paradigm: human-like appearance can elicit social responses without producing affective and semantic response patterns as rich as those elicited by HIs. No funding was received for conducting this study. The School of Business Administration, Bar-Ilan University, 5290002, Ramat Gan, Israel The authors declare no competing interests.

This study analyzed publicly available Instagram posts and comments obtained through the Meta Content Library under a research application for this project approved by Meta, in accordance with the Meta Research Tools Terms and Conditions. The study involved no interaction or intervention with users and no access to private content. Only aggregate results are reported: influencer accounts are reported under anonymized codes, and no verbatim comments or commenter identifiers are included.

The Human Subjects Institutional Review Board (HSIRB) of Bar-Ilan University determined that this study is exempt from review (exemption number 0410261303). Informed consent was not required, as only publicly available content was analyzed. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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