Characteristics of Human-Like Virtual Profiles in Relation to Audience Reach and Engagement on Instagram: Secondary Data Analysis

Clinical Snapshot

Key Findings

• P Value: Not reported - descriptive analysis only

• Effect Size: 58% of high-impression videos (>20M) from CGI-like VPs; 75% of high-engagement images featured dark hair/eyes

• Primary Outcome: Engagement rates and reach metrics for virtual profile content

• Nnt Or Sensitivity: 77.71% of top profiles demonstrated stable visual identity and consistent presentation

• Confidence Interval: Not reported

Clinical Application

Limited clinical relevance; primarily applicable to digital marketing and social media research No direct relevance to Australian healthcare practice, PBS, TGA, or RACGP guidelines Not applicable to clinical populations - social media marketing research

Abstract

BACKGROUND: Prior research on human-like virtual profiles (VPs)-computer-generated imagery (CGI; displaying legible artificiality) or artificial intelligence (AI)-generated personas on social media-is often based on small samples and limited data. Furthermore, prior studies group highly photorealistic virtual influencers with abstract virtual characters, complicating comparisons. Recent generative AI has enabled large-scale production of synthetic content. These advances have drastically increased the quality and volume of synthetic media. Much of the prior literature predates these developments, leaving open questions about how posting behavior and VP design now relate to reach and engagement outcomes. OBJECTIVE: This study aimed to examine how posting behavior and key VP attributes, namely, photorealism; physical, behavioral, and narrative consistency; and human copresence related to reach and engagement across content formats (static images vs videos) on Instagram. METHODS: A total of 157 human-like female VPs were included in the final Instagram dataset. During the initial screening stage, 5 human-like male macro and mega VPs were identified but excluded because their number was too small to support meaningful subgroup comparison. Engagement was operationalized as like rate (likes or followers) for images and videos; reach was measured via examined absolute impressions. Performance was summarized using the single best-performing post and the arithmetic mean of the top 3 posts per format. Profiles and posts were further coded using predefined content variables covering visual realism, identity consistency, copresence structure, appearance patterns, and body-type representation. RESULTS: Higher-performing content was more commonly observed among larger profiles, particularly for video metrics. Among videos exceeding 20 million impressions, out of 12 videos, 7 (58%) were produced by CGI-like VPs, whereas high video like rates were more often observed among photorealistic or face-swapped profiles. In the image engagement analysis, out of 8 highest-like-rate posts, 6 (75%) featured VPs with dark hair and dark eyes, and out of the 8 posts, 4 (50%) included copresence of multiple subjects. Furthermore, of the 157 top-performing profiles reviewed, 122 (77.71%) demonstrated stable visual identity and consistent behavioral and narrative presentation, whereas noticeable inconsistencies were rarely observed in this subset. These findings represent descriptive patterns within established macro- and mega-level profiles rather than as causal predictors of growth. CONCLUSIONS: Among established human-like female VPs on Instagram, higher engagement and reach frequently co-occurred with larger profile scale, identity coherence, and transparent virtual presentation than with photorealism alone. CGI-like aesthetics aligned more with algorithmic distribution, whereas photorealistic motion content featured more prominently among posts with high active engagement. These results suggest that audiences may respond more favorably to coherent and legible virtual identities than to ambiguous realism. Because the study was restricted to macro- and mega-level profiles, the observed traits should be interpreted as characteristics of successful incumbents rather than as general predictors of VP growth.

References

1. 1.Lebrecht, A. M., Tam, W., Merlo, O., & Eisingerich, A. B. (2026). Characteristics of Human-Like Virtual Profiles in Relation to Audience Reach and Engagement on Instagram: Secondary Data Analysis. Journal of Medical Internet Research. https://doi.org/10.1111/jcom.12237