Media framing and public risk communication: Deep Learning-based crisis narrative analysis and optimization

This research aims to develop a comprehensive framework for analyzing and optimizing media framing in crisis communication through advanced deep learning techniques, addressing the critical gap in understanding how narrative structures influence public risk perception and response. By analyzing crisis narratives across multiple media platforms, we identify predominant framing patterns and their temporal evolution during crisis events. Our novel deep learning model demonstrates superior accuracy of 91.2% in recognizing subtle framing mechanisms that influence public risk perception, representing a 14.7 percentage point improvement over traditional machine learning baselines. Analysis of 15,873 media items reveals six major frame types, with attribution frames being most prevalent (28.7%), followed by human impact (22.3%) and conflict frames (19.5%). The study establishes an optimization framework for crisis communication that balances narrative structure, emotional factors, and information transparency, identifying critical transparency-trust thresholds at 62% and 87% disclosure levels where trust gains show non-linear patterns. Findings suggest that adaptive framing strategies significantly enhance public understanding and appropriate response to risk situations, with problem-solution narratives achieving effectiveness scores of 0.87 for technological crises and empathy-focused communication reaching 0.90 for natural disasters. This research contributes to both the theoretical understanding of crisis communication and the practical applications for media organizations, risk managers, and policymakers.