VISUAL COMMUNICATION STRATEGIES IN DIGITAL CRISIS MANAGEMENT: AN AI-ENABLED MEDIA ANALYSIS APPROACH

Authors

  • Prof. Deepa Dixit Director, SIES School of Business Studies, Navi Mumbai, Maharashtra, India
  • Dr. Riya Goel Sharma Assistant Professor, School of Management and School of Advertising, PR and Events, AAFT University of Media and Arts, Raipur, Chhattisgarh-492001, India
  • Dr. Mukesh Patil Department of Management Studies, Guru Nanak Institute of Engineering & Technology, Nagpur, Maharashtra, India
  • Chandrashekhar Ramesh Ramtirthkar Associate Professor, Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra-411037, India
  • Pooja Goel Associate Professor, School of Business Management, Noida International University, Noida, Uttar Pradesh, India
  • Prof. Yogesh Nagargoje Chief Marketing Officer (CMO), Researcher Connect Innovations and Impact Private Limited, India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6959

Keywords:

Digital Crisis Communication, Visual Communication Strategies, AI-Enabled Media Analysis, Computer Vision, Misinformation Detection, Explainable Visual Analytic

Abstract [English]

This paper examines the visual communication techniques used in crisis management regarding the digital environment with the help of an AI-enhanced media analysis system that allows evaluating the tools of clarity, credibility, and emotional appeal on the platform quickly on the high-velocity. In times of crisis, the visuals that are spread through social media, news portals, and official dashboards have a potent impact in shaping the perception of people and their actions, but the evaluation is rather divided and subjective. This study aims at coming up with a scalable system that will measure the visual effectiveness and compliance with the crisis communication objectives. The given method involves the usage of computer vision, multimodal transformers, and graph-based diffusion modelling, which would be applied to analyze the image, infographics, maps, and video frames and capture and temporal contexts. Models of elements to do with salience, color semantics, iconography, spatial hierarchy, facial affect and uncertainty cues are extracted and combined with signals that relate to engagement and propagation. The performance of strategy is determined through indicators of understanding, credibility, emotional control and risk of misinformation. Multi-crisis Multi-crisis experiments on multi-crisis datasets in the fields of public health, natural disasters and infrastructure failures indicate that the framework is better at detecting misleading visuals, and design of messages, which improve in understanding predictions by up to 18% and anxiety amplifying factors, respectively, over baseline heuristics. Heatmaps and design suggestions are offered to communicators in real-time with the help of interpretable outputs. These results allow concluding that AI-based visual analytics has the potential to positively influence the crisis-related coordination, transparency, and compliance with media members and agencies, providing them with the necessary actions to follow and assisting in the work.

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Published

2025-12-28

How to Cite

Dixit, D., Sharma, R. G., Patil, M., Ramtirthkar, C. R., Goel, P., & Nagargoje, Y. (2025). VISUAL COMMUNICATION STRATEGIES IN DIGITAL CRISIS MANAGEMENT: AN AI-ENABLED MEDIA ANALYSIS APPROACH. ShodhKosh: Journal of Visual and Performing Arts, 6(5s), 632–642. https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6959