Every time you post a photo, watch the likes accumulate, and quietly decide whether your online persona is working, you are participating in one of the most intricate identity experiments ever conducted at scale. A new study published in the open-access journal Heliyon by Meng-Hsuan Wu and Yi-Qing Liao of Quanzhou Normal University offers one of the most detailed statistical portraits yet of how people manage that experiment. Drawing on survey responses from 862 adult social media users, the research shows that three distinct identity practices—projecting an online self, interpreting audience feedback, and actively rebuilding one’s digital image—each independently predict how strongly people rely on visual communication and how acutely they feel their audience watching.
The theoretical backbone of the study is Anthony Giddens’ structuration theory, a sociological framework that treats social structures and individual agency as two sides of the same coin. Structures constrain what people can do, yet those same structures only persist because people repeatedly enact them. Applied to social media, the idea is elegant: platform algorithms, interface designs, and community norms shape how users present themselves, but every post, like, and revision feeds back into those very conventions. The authors argue that self-presentation is not a one-way broadcast but a continuous negotiation between personal creativity and the rules and resources the platform provides.
To capture this negotiation empirically, the researchers designed a 21-item questionnaire measuring four constructs. Social media self-presentation captured how deliberately users construct an online persona through photos, text, and homepage curation. Feedback-driven online identity measured how much users attend to likes, comments, and shares when evaluating the identity they have built. Adaptive online self-reconstruction assessed whether people actually change their profile photos, posting styles, or visual content when their constructed image falls flat. Finally, visual communication and audience awareness gauged reliance on images and short videos over text, and the felt sense that many people are watching each post regardless of whether they respond.
The sample, collected between September 2023 and February 2024 through Chinese-language survey channels including WeChat Moments, Weibo, Facebook, Instagram, and LINE, skewed young: 63.1 percent of respondents were aged 18 to 23, and students made up 65.1 percent of the sample. Women accounted for 76.8 percent of participants. After screening out 25 straight-line responses, the researchers verified that their measurement scales were internally consistent, with Cronbach’s alpha coefficients ranging from .73 to .91, and confirmed the four-factor structure with confirmatory factor analysis in R.
The headline statistical result is striking. A simultaneous multiple regression model explained 49.1 percent of the variance in visual communication and audience awareness—remarkably high for research on human behavior. All three identity constructs emerged as significant positive predictors. Feedback-driven online identity carried the largest standardized weight (β = .314), followed by adaptive online self-reconstruction (β = .262) and social media self-presentation (β = .226). In plain terms, people who monitor audience reactions most closely are also the most attuned to visual forms of expression and the most aware of being watched.
The mediation analyses added a crucial layer. When the researchers tested whether adaptive online self-reconstruction statistically mediated the link between the other two identity practices and visual communication, the pattern was consistent with partial mediation in both cases. The coefficient for social media self-presentation dropped from .582 to .355 when the reconstruction variable entered the model, and the coefficient for feedback-driven online identity fell from .641 to .430. A structural equation model reproduced the same directional pattern, though the authors are candid that its overall fit indices—CFI of .878 and RMSEA of .086—were suboptimal, and that the cross-sectional design means these are modeled associations, not proof of causal chains unfolding over time.
Platform choice told its own subtle story. Using point-biserial correlations, the study found that Facebook, Instagram, and LINE use were weakly positively associated with self-presentation, while WeChat Moments use showed a weak negative association; Weibo, QQ Space, TikTok, Xiaohongshu, Toutiao, and Twitter showed no significant link. Posting frequency mattered too: people who posted more often scored modestly higher on all three identity constructs. In Giddens’ terms, each posting episode re-engages the platform’s rules and visibility systems, giving users another occasion to observe how their identity displays land and to reconsider what comes next. Recurrence, the authors suggest, is what keeps the structure alive.
The study also surfaced a demographic wrinkle. Students and non-students were statistically indistinguishable on all three identity-management constructs, yet non-students reported significantly higher visual communication and audience awareness (M = 3.03 versus 2.84, p = .001). The authors interpret this cautiously as a sample characteristic, but it hints that people in different life positions may inhabit the same platforms with different audience configurations and communicative habits. Age-linked platform repertoires—Weibo and TikTok among the young, Facebook and LINE among older groups—place users in structurally different visibility environments even when their underlying identity practices look similar.
What makes the findings resonate beyond the statistics is how they echo Erving Goffman’s classic dramaturgy of front-stage and back-stage behavior. Social media, the authors note, blurs that boundary: private life gets folded into public performance, and the audience’s applause—measured in engagement metrics—becomes an interpretive resource users draw on when deciding whether to maintain, revise, or entirely rebuild their digital selves. Related research cited in the paper reinforces the point, from studies showing that Instagram selfies with heavy filters attract fewer likes to work demonstrating that adolescents delete outdated posts and edit photographs to negotiate personal and social norms.
The authors are careful about limits. The convenience sample of younger, East Asian adults limits generalizability, the survey did not probe how individuals actually interpreted specific feedback signals, and algorithmic conditions were excluded from the statistical models. Still, the practical implications are tangible: the results suggest value in cultivating users’ ability to read feedback critically, recognize audience expectations, and evaluate the consequences of visual self-presentation. As visual communication increasingly displaces text as the default language of online life, understanding the reflexive loop between identity, audience, and image becomes not just an academic curiosity but a form of everyday literacy—one that this study maps with unusual statistical clarity.
Subject of Research: Statistical relationships between social media self-presentation, feedback-driven identity, adaptive self-reconstruction, and visual communication and audience awareness
Article Title: Agents’ dynamic self-presentation in relation to visual communication and audience awareness on social media
Article References: Wu, M.-H., & Liao, Y.-Q. (2026). Agents’ dynamic self-presentation in relation to visual communication and audience awareness on social media. Heliyon, 12(15), Article e45587. https://doi.org/10.1016/j.heliyon.2026.e45587
Image Credits: AI Generated
DOI: 10.1016/j.heliyon.2026.e45587
Keywords: social media, self-presentation, visual communication, audience awareness, structuration theory, online identity, Giddens, Goffman, feedback loops, survey research, structural equation modeling, Heliyon
News Source: Drew Townsend. (October 12, 2026). How Social Media Users Rewire Their Identities Through Images, Feedback and Constant Self-Revision. Scienmag.
