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Multimodal Computational Communication and Infrastructure Resilience in Digital Media Ecosystems: An Integrated Framework for Visual Analytics, Platform Interactivity, and Networked Dissemination
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Keywords
computational communication
multimodal media
computer vision
platform interactivity
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Abstract
Background: Computational communication research has moved from a niche methodological agenda to a foundational paradigm for studying digital media, platform behavior, visual representation, multimodal interaction, and large-scale communication systems (Van Atteveldt & Peng, 2018; Matei & Kee, 2019; Trilling et al., 2024; Li et al., 2025). At the same time, contemporary communication environments increasingly depend on visual automation, computer vision, multimodal fusion, platform affordances, and algorithmically mediated participation across short-video, social media, and networked information spaces (Joo et al., 2019; Wu & Mebane, 2022; Guan & Zhou, 2022; Yu & Xu, 2021). Yet a major gap remains in linking these content-centered advances to the network infrastructure conditions that sustain reliable, secure, and scalable media communication, including routing design, host adaptation, and information security architecture (Tian, 2021; Zhang, 2022; Han et al., 2023; Zeng et al., 2025).
Objective: This article develops a unified research framework that integrates computational communication analysis, multimodal media intelligence, platform interaction research, and infrastructure resilience.
Methods: Using only the supplied references, the study conducts a conceptual, architecture-oriented synthesis. It brings together scholarship on computational communication theory, computer vision in media research, social platform use and continuance, computational propaganda, visual communication automation, multimodal representations, routing configuration, network convergence, passive optical LAN systems, and information security in institutional communication environments.
Results: The analysis indicates that computational communication should be understood as a layered system rather than a single methodological toolkit. At the upper layer, communication is shaped by visual representation, user interactivity, personalization, and multimodal affordances. At the lower layer, dissemination capacity depends on routing stability, network convergence, static-dynamic configuration logic, and secure information architecture. The findings show that the analytical future of computational communication lies in integrating media-content intelligence with infrastructural awareness.
Conclusion: A next-generation computational communication framework must move beyond content analytics alone. It should connect multimodal platform analysis with network-level resilience and institutional communication security. Such an approach offers a more complete understanding of how digital communication is produced, circulated,
interpreted, and sustained in technologically intensive environments.
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