Characteristics of human-like virtual profiles in relation to audience reach and engagement on Instagram: a secondary data analysis
File(s)
Published version
Author(s)
Lebrecht, Alexandra Maria
Tam, Winze
Merlo, Omar
Eisingerich, andreas
Type
Journal Article
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.
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.
Date Issued
2026-06-05
Date Acceptance
2026-04-10
Citation
Journal of Medical Internet Research, 2026, 28
ISSN
1439-4456
Publisher
JMIR Publications
Journal / Book Title
Journal of Medical Internet Research
Volume
28
Copyright Statement
© Alexandra Maria Lebrecht, Winze Tam, Omar Merlo, Andreas Benedikt Eisingerich. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.Jun.2026. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
License URL
Identifier
10.2196/86233
Subjects
virtual influencers
artificial intelligence
generative AI
Instagram
audience engagement
audience reach
photorealism
content analysis
ethical design
computer-generated image
Publication Status
Published
Article Number
e86233