Multimodal Large Language Models for Understanding Human Self-Concept Evolution in Digital Social Environments

Abstract

The digitally mediated social world is now a primary arena in which human self-concept is formed, revised, and expressed. The proliferation of multimodal platforms, from image-centric social networks to short-video ecosystems, has transformed identity construction into a continuous, cross-channel, and algorithmically shaped process. Understanding how individuals’ self-representations evolve across these environments poses profound challenges for methods rooted in single-modality analysis or episodic self-report. This paper examines the potential of multimodal large language models (MLLMs) as systems instruments capable of capturing the textured, longitudinal dynamics of self-concept within digital social environments. We adopt a systems-level perspective that moves beyond model accuracy to interrogate the broader sociotechnical infrastructure required to deploy MLLMs responsibly. The discussion is organized around structural trade-offs, data governance, architectural choices, fairness considerations, robustness, sustainability, and policy implications. We argue that MLLMs, by virtue of their capacity to process and integrate text, image, video, and interaction metadata at scale, can serve as computational mirrors that reveal how platform affordances and algorithmic curation co-produce evolving self-views. However, harnessing this capacity demands rigorous attention to the inherent tensions between representational richness and privacy, between personalization and ecological validity, and between interpretability and model scale. The paper provides a conceptual analysis of these tensions, sketches design principles for a federated MLLM-based research infrastructure, and maps the regulatory landscape that must be navigated if such systems are to be deployed in ways that uphold human autonomy, fairness, and long-term trust.