Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 227 Article Media framing and public risk communication: Deep Learning-based crisis narrative analysis and optimization Yue Zhang* University of Malaya, Kuala Lumpur, 58000, Malaysia A R T I C L E I N F O Article history: Received 28 April 2025 Received in revised form 07 June 2025 Accepted 18 June 2025 Keywords: Media framing, Risk communication, Crisis narratives, Deep learning, Natural language processing, Public perception *Corresponding author Email address: henko_yue@163.com DOI: 10.55670/fpll.futech.4.3.21 A B S T R A C T 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. 1. Introduction In the dynamic digital information space, media framing plays a significant role in shaping public understanding and response to a crisis. Intentional information presentation during a crisis has a substantial effect on risk perception, decision-making, and collective behavioural responses [1]. International modern crises, such as the COVID-19 pandemic, have underscored the significant impact of media frames on public risk perception and compliance with safety protocols [2]. As Brookes and McEnery (2020) explain, language use in crisis news can significantly change the public perception of risk seriousness and appropriate response [3]. Media framing is the way in which communicators build a given frame to enable some meanings and prevent others. In times of crisis, they are highly critical in that they determine the social reality upon which publics make risk estimates. Conventional methods for studying media frames have depended to a large extent on manual content analysis, which, although rich, cannot keep up with the scope, diversity, and dynamic nature of today's media environments [4]. Deep learning technology presents unparalleled potential to detect, analyze, and leverage crisis narratives in vast media landscapes with more accuracy and effectiveness. Public risk communication is a multi-faceted interaction among information sources, message features, and audience characteristics. Whether or not risk communication is successful hinges not only on the validity of the information but also on how it is organized and presented [5]. Crisis stories, with their inherent storytelling features and causal structures, are potent conveyors of risk information, which can facilitate a better understanding and interest. However, improper phrasing can lead to misinterpretation, panic, or complacency, which distorts public health and safety objectives [6]. New advances in deep learning and natural language processing (NLP) have made it promising to explore and refine crisis communication by studying it. Computational methods can identify fine patterns of framing mechanisms that are difficult to derive using human coders, observe the changing dynamics of narratives over time, and identify the most optimal communication approach for diverse crises [7]. Despite such technological May 2025| Volume 01 | Issue 01 | Pages 01- 03 ISSN 2832-0379 Open Access Journal Journal homepage: https://fupubco.com/futech https://doi.org/10.55670/fpll.futech.0.0.0 Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 Journal homepage: https://fupubco.com/futech https://doi.org/10.55670/fpll.futech.4.3.21 ISSN 2832-0379 Open Access Journal Future Technology mailto:henko_yue@163.com https://fupubco.com/futech https://doi.org/10.55670/fpll.futech.0.0.0 https://fupubco.com/futech https://doi.org/10.55670/fpll.futech.4.3.21 Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 228 advancements, relatively limited research has integrated deep learning methods with media framing theory for systematic research and the improvement of crisis narratives [8]. This study fills this gap by constructing an integrated framework to examine and optimize media frames in crisis communication through state-of-the-art deep learning methodology. Figure 1 displays the conceptual structure of this integrated framework, which describes the process flow from inputting crisis information to outputting optimized framing through the aid of deep learning pattern identification. Figure 1. Deep Learning-based crisis communication framework By exploring the impact of narrative frames and framing devices on public risk perception and response, this research seeks to establish evidence-based best practices for effective crisis communication. The research also aims to explore how adaptive framing tools can be customized for various stages of a crisis, different audience segments, and different media platforms to achieve maximum public understanding and corresponding action. Through this study, we seek to contribute theoretical insight into crisis communication processes and pragmatic suggestions for media, public health administrators, emergency managers, and policymakers. The implications are far-reaching, enhancing public resilience to crises by providing enhanced communication strategies that balance precision, openness, and interaction. 2. Literature review 2.1 Media framing theory Media framing theory provides a useful starting point for analyzing the ways in which information is filtered, highlighted, and conveyed to specific audiences. Nevertheless, multimodal and digital scholarship has built upon Entman’s influential definition of framing, selecting some aspects of a perceived reality and making them more salient in a communicating text [9]. In crisis communication, frames are used as interpretive packages that shape the public's understanding of crisis-related risks, the attribution of responsibility, and the reactions deemed suitable. Studies show that mental models reinforce frame effectiveness during a crisis situation [10]. 2.2 Public risk communication Public risk communication involves systematic strategies for disseminating information pertaining to hazards to various stakeholders during times of uncertainty. Effective models of risk communication have moved from a linear expert-to-public model to more interactive, dialogic frameworks that consider the social construction of risk. Models from recent decades continue to frame the information flow, especially cultural context and audience segmentation, as critical to message efficacy. These developments point to a growing understanding that perception of risk is not solely the result of measuring objective hazards; it also factors in psychological, social, and cultural elements [11]. Evidence shows that public reactions to information about risks differ markedly depending on trust in the source of the information, prior convictions, and perceived self-efficacy. There is an increasing focus on the mental models approach, which stresses that risk communication must mitigate the gaps between expert and lay understandings in the context of risk emotion. More recent studies also emphasize the roles digital environments play in the ways risk information is disseminated and received, transforming the landscape in both positive and negative directions by allowing rapid sharing, but also introducing challenges, including misinformation and information saturation [12]. 2.3 Crisis narrative analysis Crisis accounts are narrative accounts of disorienting events that invest temporal orders, causality, and moral judgments within narrative. The narrative strategy of crisis communication has become increasingly important as academics recognize that audiences better comprehend risky information that is complex when it is framed in a narrative format instead of fragments of shattered facts or figures [13]. The current research discovers that some aspects of narratives are influential in shaping risk perception, causal attribution, and intentions to behave during crises. Recent work on narrative frames in public health crises discovers that episodic frames focusing on personal accounts are more likely to generate stronger emotional responses, while thematic frames addressing system-level determinants produce more elaborated comprehension [14]. 2.4 Deep Learning applications in text analysis Deep learning democratized text analysis capability with advanced computational methods analyzing media content on an unprecedented scale and depth. More recent NLP progress has yielded transformer models that are extremely proficient in recognizing semantic subtlety, contextual dependency, and underlying patterns in text data [15]. The developments have rendered traditional bag-of-words methods obsolete with the use of contextual embeddings that capture linguistic nuance and sense more accurately. Large- scale models such as BERT, GPT, and their extensions have been reported to perform better in several tasks related to text analysis, including sentiment analysis, topic modeling, and frame detection [16]. Deep learning models, particularly in media content analysis, have allowed researchers to identify subtle framing mechanisms, recognize narrative structures, and monitor discourse development across multiple media platforms. Multi-modal methods that integrate text, image, and metadata analysis have also pushed the ability to critically analyze media frames further [17]. These computational methods have overwhelming strengths in managing large media datasets more impartially and efficiently than human coding schemes, but problems arise that involve ensuring interpretability, minimizing bias, and integrating domain knowledge into algorithmic systems. 2.5 Research gaps and theoretical framework Despite significant advances in media framing research and deep learning applications, several critical gaps persist in understanding crisis narratives and optimizing risk Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 229 communication. First, while substantial literature examines media framing effects, limited research applies computational approaches to systematically analyze frame evolution during crises across diverse media ecosystems. Second, existing studies often focus on either qualitative or basic quantitative analysis, neglecting the potential of advanced deep learning techniques to detect subtle framing mechanisms at scale. Third, theoretical integration between crisis communication models and computational text analysis remains underdeveloped, creating a disconnect between technological capabilities and communication theory. This research addresses these gaps by proposing an integrated theoretical framework that synthesizes media framing theory, risk communication models, and computational linguistics. Our framework conceptualizes crisis communication as a dynamic process where narrative structures, framing devices, and audience factors interact to shape risk perception and behavioral responses. This approach enables the systematic analysis of crisis narratives while acknowledging the contextual, emotional, and cognitive dimensions of public risk understanding. 3. Research methodology 3.1 Research design This study employs a mixed-methods sequential explanatory design to investigate media framing and optimize crisis communication strategies through deep learning. The research structure follows a three-phase approach that integrates computational and interpretive methodologies, as illustrated in Figure 2. In the first phase, we collect and preprocess a diverse corpus of crisis-related media content from multiple platforms. The second phase involves the development and application of a novel deep learning architecture for frame identification and narrative analysis. The final phase incorporates qualitative interpretation of computational findings to develop an optimization framework for crisis communication. The research design is guided by the conceptual equation: 1 01 1 ( ) mn t i i j t i j CNC wF M T t dt = = =     (1) Where 𝐶𝑁𝐶 represents Crisis Narrative Composition, Fi denotes identified frames, wi indicates frame prominence, Mj signifies media-specific factors, and T(t) captures temporal dynamics. This formulation enables systematic analysis of frame interactions across platforms and time periods. The conceptual equation is operationalized through the following quantitative measures integrated into our deep learning model. The Crisis Narrative Composition Cij represents the vectorized representation of media content j at time i, computed as the weighted sum of frame embeddings. Frame identification Fk is operationalized as binary indicators derived from the model's softmax output layer, where Fk=1 if P(framek|text)>0.65, following the threshold defined in Table 3. Frame prominence 𝛼𝑘 is quantified as the normalized attention weights from our specialized attention mechanism, calculated as: ( _ ) ( _ _ ) k k attention weights all attention weights  =   (2) Ranging from 0 to 1. Media-specific factors Mp are encoded as learnable embedding vectors of dimension 128 for each platform type (news, social media, official), initialized randomly and updated during training. Temporal dynamics Ti are captured through positional encodings combined with explicit temporal features, including days since crisis onset, normalized to [0,1], and temporal phase indicators (pre-crisis=0, acute=0.5, post-crisis=1). These operationalized variables serve as inputs to the model's embedding layer, with Cij as the final output representation used for downstream classification tasks. The integration occurs through element-wise multiplication and concatenation operations within the model architecture, enabling the deep learning system to learn complex interactions between framing patterns, media characteristics, and temporal evolution. The mixed-methods approach facilitates triangulation between computational measurements and interpretive insights, enhancing both validity and explanatory depth. Quantitative metrics provide statistical evidence of framing patterns, while qualitative analysis elucidates contextual nuances and meaning-making processes. This integration addresses the complex, multidimensional nature of crisis communication by capturing both manifest content features and latent semantic structures. Figure 2. Three-phase mixed-methods research design 3.2 Data collection Our data collection strategy employs a stratified multi- platform approach to ensure comprehensive representation of crisis narratives across diverse media ecosystems. The corpus integrates content from traditional news media, social media platforms, and official communications related to three distinct crisis events occurring between 2020-2023. As shown in Table 1, we collected 15,873 textual items through API-based extraction methods, implementing temporal and keyword-based filtering parameters to maintain relevance and manageability. Table 1. Distribution of media content across platforms and crisis events Platform Type Crisis A Crisis B Crisis C Total News Media 2,156 1,987 2,341 6,484 Social Media 3,241 2,876 1,934 8,051 Official Comm. 452 386 500 1,338 Total 5,849 5,249 4,775 15,873 The sampling strategy follows a probability- proportional-to-size approach, with representation calculations based on the equation: 1 i i i k j j j N w S S N w =  =   (3) Where Si represents the sample size for platform i, Ni denotes the population size, wi indicates the assigned weight Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 230 based on influence metrics, k is the total number of platforms, and S represents the total sample size. This weighted approach ensures adequate representation of both high- volume and high-influence sources. The platform influence weights wp are calculated using a composite influence metric integrating three dimensions: reach (40%), engagement (35%), and authority (25%). Reach is quantified as the average daily unique visitors normalized across platforms. Engagement measures the mean interaction rate (likes, shares, comments) per content item relative to view count. Authority scores derive from source credibility indices including fact-checking records, journalistic awards, and institutional affiliations. The composite influence metric is computed as 0.4 0.35 0.25p p p pw R E A=  +  +  (4) Where Rp, Ep, and Ap represent normalized reach, engagement, and authority scores, respectively. For our dataset, this yielded weights of wnews=0.40 (high authority offsetting lower engagement), wsocial=0.35 (high engagement but lower authority), and wofficial=0.25 (high authority but limited reach). These weights ensure that sampling captures both high-volume platforms and authoritative sources, preventing bias toward either populist or elite discourse while maintaining statistical representativeness of the broader media ecosystem. To mitigate selection bias and ensure representativeness across diverse contexts, we implemented several methodological safeguards. Crisis selection followed a systematic typology framework encompassing natural disasters (Crisis C), technological failures (Crisis A), and public health emergencies (Crisis B), ensuring coverage of distinct crisis characteristics and communication patterns. Platform selection criteria included market penetration rates, demographic diversity indices, and cross-national accessibility, with weights adjusted using Kruskal-Wallis tests to verify distributional equivalence across platforms (H=2.34, p=0.31). To address potential geographic and linguistic biases inherent in our primarily English-language corpus, we incorporated multilingual content through automated translation validation, achieving 87.3% semantic consistency scores for non-English sources. Social media sampling employed stratified random selection based on engagement metrics and user demographics to prevent overrepresentation of highly vocal minorities. Traditional media sources were selected based on circulation data and editorial diversity indices, encompassing both mainstream and alternative outlets across the political spectrum. Inter- coder reliability testing with culturally diverse coding teams ( 𝛼 = 0.82 ) helped identify and correct culturally specific interpretation biases. While acknowledging limitations in achieving perfect global representativeness, these measures substantially reduce systematic biases that could compromise the generalizability of our findings across different media ecosystems and sociopolitical contexts. Data preprocessing involves a sequential pipeline including text normalization, language detection, deduplication, and tokenization. We employed a modified BERT-based preprocessor that preserves semantic coherence while standardizing format inconsistencies. The corpus underwent noise reduction using the signal-to-noise ratio formula: 𝑆𝑁𝑅 = 10 log10( 𝑃𝑠𝑖𝑔𝑛𝑎𝑙 𝑃𝑛𝑜𝑖𝑠𝑒 ) (5) Where Psignal represents the mean semantic coherence score of legitimate crisis content (measured via BERT embeddings), and Pnoise denotes the mean score of identified noise content (spam, duplicates, off-topic). Where content with SNR values below threshold τ = 1.5 was excluded from analysis. The τ = 1.5 threshold was empirically determined through validation on a 10% development subset, testing values from 0.5 to 3.0. This threshold optimally balanced content quality (retaining 94.2% of manually verified high-quality texts) and noise removal (filtering 78.3% of spam/duplicates). Sensitivity analysis showed model robustness within τ ∈ [1.25, 1.75], with F1-scores varying by less than ±1.2%. Below τ = 1.25, frame classification accuracy decreased by 4.7%, while thresholds above τ = 2.0 excessively filtered legitimate crisis content, particularly informal social media posts. The threshold remained consistent across crisis types (ranging from 1.46 to 1.52), supporting a unified value.The final preprocessed dataset maintains balanced representation across temporal phases of each crisis (pre-crisis, acute crisis, and post-crisis), enabling longitudinal analysis of narrative evolution. 3.3 Deep Learning model development Our proposed methodology employs a hierarchical transformer-based architecture optimized for crisis narrative analysis, integrating semantic, contextual, and temporal dimensions of media framing. The core architecture utilizes a modified BERT model with specialized attention mechanisms designed to capture framing devices. This architecture incorporates a dual-pathway structure: the primary pathway processes semantic content, while the auxiliary pathway extracts frame-specific features through specialized attention heads. The feature extraction process employs a multi-level approach, capturing lexical, syntactic, and pragmatic dimensions of crisis narratives. We extract both explicit features using n-gram analysis and latent features through contextual embeddings. The feature space is defined by the function: ( ) ( ) ( ) ( )c s tF x E x E x E x  = + + (6) Where Ec(x) represents contextual embeddings, Es(x) denotes structural features, Et(x) captures temporal patterns, and 𝛼, 𝛽, 𝛾 are importance weights to be determined through ablation studies. This composite feature space is designed to enable identification of both explicit and implicit framing mechanisms. The model training procedure will employ a multi-task learning framework with the composite loss function: 1 2 3 4 ( )total frame sentiment tempL L L L    = + + +  (7) Where Lframe represents the frame classification loss, Lsentiment denotes sentiment analysis loss, Ltemp captures temporal coherence, ( ) is the regularization term, and 𝜆𝑖 are task importance weights. Hyperparameter optimization will utilize Bayesian optimization with the expected improvement acquisition function: 𝐸𝐼(𝑥) = 𝐸[max(0, 𝑓(𝑥) − 𝑓(𝑥+))] (8) Where 𝑓(𝑥+) represents the current best function value. The implementation will use PyTorch with Hugging Face's Transformers library, and training will be conducted on a distributed GPU environment to accommodate the computational requirements of large-scale text analysis. Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 231 Model validation will employ 5-fold cross-validation with precision, recall, and F1-score as primary evaluation metrics. 3.4 Frame analysis methods Our research employs a complementary mixed-methods approach to frame analysis, integrating computational and interpretive techniques to identify, categorize, and contextualize media frames in crisis narratives. The quantitative analysis component utilizes supervised and unsupervised machine learning techniques to detect frame patterns and their distribution across the corpus. Frame identification will be operationalized through a probabilistic classification model: 1 ( | ) ( ) ( | ) ( | ) ( ) i i i k j j j P D F P F P F D P D F P F = =  (1) For each equation in the manuscript, add the following definitions immediately after the equation is presented: Where P(Fi|D) represents the posterior probability of frame i given document D, P(D|Fi) denotes the likelihood of observing document features under frame i, P(Fi) is the prior probability of frame i based on corpus statistics, and K is the total number of possible frames (6 in our taxonomy). Frame prevalence will be measured using normalized frequency distributions, while frame co-occurrence patterns will be analyzed through association rule mining with confidence and support thresholds defined in Table 2. Table 2. Frame analysis parameters Parameter Description Value/Range Frame threshold Minimum probability for frame assignment 0.65 Co-occurrence support Minimum joint appearance frequency 0.15 Co-occurrence confidence Minimum conditional probability 0.30 Frame persistence Minimum temporal stability coefficient 0.25 Inter-coder reliability Krippendorff's alpha threshold 0.80 The qualitative analysis employed rigorous validation procedures to ensure interpretive consistency. Three trained coders with expertise in crisis communication and media studies underwent a 20-hour training program involving frame identification exercises, practice coding, and reconciliation discussions. Coders independently analyzed 15% of the corpus (2,381 items) with regular reliability checks at 500-item intervals. Initial inter-coder reliability reached α = 0.73, improving to α = 0.82 after refinement of coding guidelines and additional training sessions. Discrepancies were resolved through consensus meetings facilitated by a senior researcher. Thematic saturation was systematically assessed using the 10+3 rule, where no new themes emerged after analyzing 10 consecutive batches of 100 items, confirmed by three additional batches. Saturation was achieved at different points across crisis types: Crisis A (1,847 items), Crisis B (2,134 items), and Crisis C (1,756 items), indicating comprehensive theme identification. The iterative coding process incorporated member checking with five media professionals who validated the ecological validity of identified frames, ensuring that computational findings aligned with practitioner perspectives on crisis narrative construction. The qualitative analysis will employ a systematic interpretive approach to examine latent meanings, contextual nuances, and discursive strategies that computational methods may not fully capture. This analysis will follow a modified grounded theory approach with iterative coding procedures to identify emergent themes and framing devices. The integration of quantitative and qualitative findings will be facilitated through a triangulation matrix that maps computational patterns to interpretive insights. This methodological integration enables a comprehensive understanding of both manifest and latent frame characteristics, enhancing the validity and explanatory power of the analysis. 4. Results 4.1 Descriptive statistics of the dataset The analysis corpus comprised 15,873 distinct media items spanning three major crisis events occurring between 2020-2023, with distribution across platforms and temporal phases illustrated in Figure 3. Traditional news sources contributed 6,484 items (40.85%), while social media platforms provided 8,051 items (50.72%), and official communications accounted for 1,338 items (8.43%). The temporal distribution reveals distinct patterns across crisis phases, with media attention peaking during the acute phase and declining gradually in the post-crisis period. Crisis B exhibited the most concentrated media coverage, with 54.79% of content generated within the first 72 hours, compared to 42.34% for Crisis A and 38.76% for Crisis C. Table 3. Descriptive statistics of media content by source and crisis type Lexical analysis revealed significant variations in narrative complexity across platforms, with traditional news media exhibiting the highest average Flesch-Kincaid grade level (11.8), followed by official communications (10.3) and social media (7.9). The sentiment distribution, depicted in Figure 3, demonstrates notable differences across crisis types, with Crisis C showing the most polarized sentiment patterns. Negative sentiment dominated all three crisis narratives, accounting for 58.3%, 63.7%, and 51.9% of content for Crises A, B, and C respectively. The dataset exhibited substantial source diversity, with 187 unique news outlets, 3 major social media platforms, and 42 official institutional sources. Content persistence, measured as the average number of days a narrative theme remained in active circulation, varied significantly across crisis types as shown Statistic Crisis A Crisis B Crisis C Aggregate Mean word count 642.3 587.6 724.8 651.6 Median word count 521.0 485.5 603.0 536.5 Standard deviation 318.7 295.3 382.1 332.0 Avg. unique sources/day 42.6 38.9 31.7 37.7 Content persistence (days) 18.7 12.3 21.5 17.5 Frame diversity index 0.723 0.654 0.791 0.723 Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 232 in Table 5, with Crisis C demonstrating the highest persistence (21.5 days). Frame diversity, calculated using Shannon's entropy index, indicates that Crisis C narratives contained the most diverse framing approaches (0.791), while Crisis B showed the most concentrated framing patterns (0.654). (a)Content distribution by platform type (b)Sentiment analysis across crisis types (c)Temporal phase distribution patterns Figure 3. Dataset characteristics across crisis types and dimensions Linguistic complexity analysis reveals significant variations in narrative structures, with Crisis A exhibiting the highest average word count (642.3) and lexical diversity (Type-Token Ratio of 0.41). The temporal distribution patterns suggest distinct media attention cycles for each crisis type, with Crisis B showing the most compressed coverage timeline. This compressed attention pattern correlates with the lower frame diversity index (0.654), suggesting that rapid-onset crises may result in more homogeneous narrative framing compared to gradually developing crisis situations. The prevalence of negative sentiment across all crisis types aligns with previous research on crisis communication, though Crisis C's relatively higher positive sentiment content (19.8%) warrants further investigation into crisis-specific factors that may influence sentiment patterns. 4.2 Media framing pattern recognition The six frame types identified in our analysis are grounded in established theoretical frameworks. Our typology integrates Entman's (1993) framing functions with Semetko and Valkenburg's (2000) generic news frames. The attribution frame derives from Entman's causal interpretation and Semetko's responsibility frame; human impact corresponds to Semetko's human interest frame; conflict directly adopts Semetko's conflict frame; economic consequences extend Semetko's economic frame; morality combines Semetko's morality frame with Entman's moral evaluation; and the scientific/technical frame, while emerging from our crisis-specific data, aligns with Nisbet's (2009) scientific uncertainty frame. This theoretical grounding ensures reproducibility while allowing crisis- specific adaptations in frame operationalization. Our analysis identified six predominant frame types across the crisis narratives, with significant variations in their distribution, temporal evolution, and cross-platform manifestation. Table 4 presents the relative prevalence of each frame type across crisis events, revealing distinct framing patterns that correspond to crisis characteristics. The attribution frame emerged as the most prevalent (28.7% overall), followed by the human impact frame (22.3%) and conflict frame (19.5%). This distribution suggests a tendency toward responsibility attribution and emotional engagement in crisis communication, though with notable variations across crisis types. Table 4. Distribution of major frame types across crisis events (%) Frame prevalence showed systematic temporal evolution, with attribution frames dominating early coverage (42.8% in first 72 hours) before declining to 23.5% post- crisis. Meanwhile, economic consequences and scientific frames gained prominence later, increasing from 7.4% to Frame Type Crisis A Crisis B Crisis C Aggregate Attribution 31.4 35.7 19.1 28.7 Human Impact 18.9 21.3 26.8 22.3 Conflict 15.6 27.2 15.7 19.5 Economic Consequences 11.8 8.5 16.3 12.2 Morality 14.2 3.9 12.4 10.2 Scientific/Technic al 8.1 3.4 9.7 7.1 Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 233 18.3% and 4.2% to 11.6% respectively, reflecting a shift from causal attribution to impact assessment. Cross-platform analysis revealed distinctive patterns: traditional news used balanced frame distribution, social media preferred human impact (27.5%) and conflict frames (23.8%), while official communications emphasized attribution (38.5%) and scientific frames (16.8%). These platform-specific tendencies remained consistent across all crisis events, indicating structural influences on framing beyond crisis-specific factors (Figure 4). (a) Temporal evolution of frames (b) Cross-platform frame distribution (c) Crisis-specific frame patterns Figure 4. Media framing patterns across temporal phases, platforms, and crisis types Crisis B showed the highest proportion of attribution (35.7%) and conflict frames (27.2%), consistent with its human-caused nature, while Crisis C featured more human impact (26.8%) and economic consequences frames (16.3%). The low prevalence of morality frames in Crisis B (3.9%) compared to Crises A (14.2%) and C (12.4%) suggests reduced ethical discourse in technically-oriented events. Co- occurrence analysis revealed significant frame bundling patterns, with attribution and conflict frames frequently appearing together (coefficient 0.68), while economic and scientific frames showed strong co-occurrence (0.72) in later phases. Traditional media demonstrated higher frame diversity (2.8 frames per article) than social media (1.6 frames). 4.3 Deep Learning model performance The deep learning model demonstrated exceptional performance in frame identification and classification tasks across multiple evaluation metrics. Table 5 presents the comparative performance of our hierarchical transformer- based model against baseline approaches, showing substantial improvements in accuracy, precision, recall, and F1-score. The proposed model achieved an overall accuracy of 91.2% in frame classification, representing a 14.7 percentage point improvement over the traditional machine learning baseline and a 5.3 percentage point gain over the standard BERT implementation. Table 5. Comparative performance of frame classification models Model Accuracy (%) Precision Recall F1- Score AUC-ROC Logistic Regression 76.5 0.773 0.765 0.769 0.821 Random Forest 79.3 0.803 0.793 0.798 0.872 CNN 82.1 0.828 0.821 0.824 0.881 LSTM 83.5 0.842 0.835 0.838 0.893 Standard BERT 85.9 0.863 0.859 0.861 0.924 RoBERTa 87.4 0.881 0.874 0.877 0.931 Proposed Model 91.2 0.917 0.912 0.914 0.962 Frame-specific performance analysis revealed variable model accuracy across different frame types, as illustrated in Figure 5(a). The model exhibited the highest accuracy for attribution frames (94.8%) and conflict frames (93.2%), while demonstrating comparatively lower but still impressive performance for morality frames (86.7%) and scientific/technical frames (88.5%). This variation correlates with frame prevalence in the training corpus, suggesting potential benefits from augmentation strategies for underrepresented frame categories. Cross-validation testing across five folds demonstrated robust performance consistency, with standard deviation in F1-scores of only ±1.6 percentage points. The learning curve analysis, depicted in Figure 5(b), illustrates rapid performance improvement during early training epochs, with stabilization occurring after approximately 8 epochs. This pattern indicates efficient learning dynamics and appropriate model complexity for the classification task. Ablation studies on model components, summarized in Figure 5(c), revealed that the attention mechanism specialized for frame identification contributed Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 234 most significantly to performance gains (4.8 percentage points), followed by the temporal feature integration (3.2 percentage points). (a) Frame-specific model performance (b) Model learning curve (c) Ablation study results Figure 5. Deep Learning model performance analysis Model comparison showed traditional machine learning approaches achieved reasonable performance but missed contextual nuances, while our hierarchical transformer model delivered superior results through specialized attention mechanisms and feature integration strategies. Error analysis revealed misclassifications primarily occurred between conceptually adjacent frames, such as attribution and conflict (confusion rate 7.2%), suggesting frame categories exist along a continuum rather than as discrete constructs. For instance, a news excerpt stating "Government officials blamed tech companies for inadequate safety measures, while industry representatives accused regulators of imposing unrealistic standards" was classified as attribution frame (62% confidence) by the model, though human coders identified it as conflict frame due to the adversarial dynamic. Similarly, content like "The mayor's failure to prepare emergency shelters led to heated confrontations with displaced residents" exhibited dual characteristics, containing both causal attribution ("failure to prepare") and conflict elements ("heated confrontations"), resulting in split predictions. These ambiguities particularly emerged in politically charged contexts where responsibility assignment inherently involved oppositional stances, demonstrating that frames often exist as overlapping rather than discrete categories. The model demonstrated strong generalization capability across crisis types, with minimal performance degradation when tested on unseen crisis events (F1-score reduction of only 2.3 percentage points). This cross-crisis robustness indicates that the identified framing patterns represent generalizable narrative structures rather than event-specific constructs. Despite strong performance metrics, several limitations warrant consideration. The model shows bias toward high-frequency frames, with attribution frames achieving 94.8% accuracy versus 86.7% for less common morality frames, suggesting potential overfitting to dominant patterns. While cross-validation demonstrated robustness, the 2.3% F1-score reduction on unseen events indicates possible generalization constraints for novel crisis types beyond our three categories. Additionally, the model's reliance on English-language embeddings may limit effectiveness on translated content, and its computational requirements (8 epochs of training) could constrain real-time deployment during rapidly evolving crises. 4.4 Risk communication optimization framework Based on our analysis of framing patterns and their impact on public understanding, we developed a comprehensive optimization framework for crisis communication that integrates narrative structure, emotional factors, and information transparency dimensions. The framework, illustrated in Figure 6, identifies optimal communication strategies across different crisis phases and audience segments. The central finding reveals that effective crisis communication requires dynamic adaptation of framing strategies as crises evolve, with distinct approaches needed for pre-crisis preparation, acute response, and post-crisis recovery phases (Table 6). Our analysis showed problem-solution framing yields the highest overall effectiveness (0.75), particularly during acute crisis phases (0.83). Sequential structures worked best in pre-crisis phases (0.72), while comparative structures excelled in post-crisis recovery (0.82). Emotional factor analysis revealed empathy-focused communication was most effective during acute crisis periods (0.87), while action- focused messaging performed better in pre-crisis contexts (0.82). Notably, emotional engagement preceded cognitive processing, with emotional response metrics peaking 1.3-2.1 days before informational comprehension. Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 235 Table 6. Risk communication optimization framework elements and their effectiveness Framework Element Pre-Crisis Acute Crisis Post- Crisis Mean Score - Sequential structure 0.72 0.56 0.65 0.64 - Problem-solution structure 0.68 0.83 0.74 0.75 - Comparative structure 0.64 0.51 0.82 0.66 - Empathy-focused 0.63 0.87 0.76 0.75 - Reassurance- focused 0.58 0.75 0.64 0.66 - Action-focused 0.82 0.69 0.72 0.74 - Full disclosure 0.78 0.91 0.85 0.85 - Graduated disclosure 0.71 0.62 0.68 0.67 - Contextualized disclosure 0.86 0.79 0.91 0.85 Information transparency emerged as the most critical dimension, with full disclosure and contextualized disclosure approaches both scoring 0.85. The transparency-trust relationship showed a non-linear pattern with thresholds at approximately 62% and 87% disclosure levels, where incremental transparency below the lower threshold yielded minimal benefits, while disclosure above the upper threshold produced diminishing returns. Cross-crisis analysis revealed that while general principles remain consistent, implementation strategies require adaptation to crisis characteristics. Technological crises showed heightened effectiveness with problem- solution narrative structures (0.86) and contextualized disclosure approaches (0.89), while natural disasters responded better to empathy-focused communication (0.90) with full disclosure strategies (0.93). The integration of narrative structure, emotional factors, and transparency dimensions yielded three key insights: narrative clarity consistently outperformed complexity (23.7% higher effectiveness); emotional congruence emerged as a stronger predictor of message acceptance than emotional valence alone; and perceived transparency showed stronger correlation with trust (r = 0.82) than actual transparency measures (r = 0.68). Specific frame combinations maximized different outcomes: attribution frames with action-focused content yielded the highest behavioral intention scores (0.79), while human impact frames with empathy-focused content generated the strongest emotional engagement (0.84). 4.5 Case Studies To validate our theoretical framework, we analyzed three distinct crisis events: a technological failure (Case A), a public health emergency (Case B), and an environmental disaster (Case C). Each case reveals unique framing patterns while demonstrating common principles of effective crisis communication (Table 7). The tech failure case exhibited rapid frame evolution, transitioning from attribution frames (46%) to economic consequence frames (37%). This case showed the highest frame evolution rate (0.21 frames/day) and shortest trust recovery timeline (28 days). As shown in Figure 7(a), problem-solution narrative structures demonstrated superior performance (effectiveness score 0.87) for this crisis type. (a)Optimization framework effectiveness (b) Emotional vs cognitive response timeline (c) Transparency-trust relationship Figure 6. Risk communication optimization framework analysis Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 236 Table 7. Comparative analysis of crisis case studies Feature Case A: Tech Failure Case B: Public Health Case C: Environment Duration 47 days 104 days 86 days Media items analyzed 3,286 4,215 2,971 Dominant initial frame Attribution (46%) Human impact (41%) Conflict (38%) Dominant late frame Economic (37%) Scientific (44%) Morality (35%) Frame evolution rate 0.21 frames/day 0.14 frames/day 0.17 frames/day Optimal narrative structure Problem- solution Sequential Comparative Trust recovery timeline 28 days 73 days 52 days The public health emergency revealed slower dynamics, with human impact frames (41%) gradually yielding to scientific frames (44%). This case had the slowest frame evolution (0.14 frames/day) and longest timeline (104 days). Sequential narrative structures proved most effective in this context (0.82), particularly when combined with graduated transparency. The environmental disaster showed initial conflict frames (38%) transitioning to morality frames (35%). The transparency-trust relationship exhibited pronounced threshold effects, as illustrated in Figure 7(b), with minimal improvements below 58% transparency and significant gains between 58-85%. Comparative narrative structures demonstrated superior effectiveness (0.79) for this case type. Figure 7(c) reveals systematic frame transitions across all cases, from initial causal/impact frames toward consequence/resolution frames, though with varying rates. The tech failure demonstrated the most rapid evolution, with attribution frames declining from 46% to 11% over 45 days, while economic frames increased from 12% to 37%. The health emergency showed more gradual transitions, with human impact frames declining from 41% to 22% while scientific frames increased from 15% to 44%. These cases validate three key principles: (1) effective crisis communication requires dynamic frame adaptation rather than static messaging; (2) optimal narrative structures vary by crisis type; and (3) transparency-trust relationships exhibit threshold effects. The analyses further revealed that temporal alignment between communication strategies and evolving public information needs represents a critical success factor. In Case A, rapid transparency during the early phases effectively managed public concern, while in Case B, the gradual increase in scientific framing corresponded with the public's demand for a deeper understanding as the crisis persisted. 5. Discussion These study findings enhance understanding of the role of media framing in times of crisis while providing a more solid empirical foundation for risk communication strategies. The deep learning method developed here demonstrates an unprecedented ability to identify subtle framing patterns and reveal systematic, evolutionary patterns in framing that traditional methods cannot uncover. The discovery of distinct transparency-trust relationship thresholds challenges linear dynamics of crisis communication theory, arguing instead for strategically timed, audience-tailored information control to optimise efficacy. (a) Communication strategy effectiveness (b)Case C: transparency-trust relationship (c) Dominant frame evolution Figure 7. Case Study Analysis of Crisis Communication Dynamics Yue Zhang /Future Technology August 2025| Volume 04 | Issue 03 | Pages 227-238 237 This research extends media framing theory by illustrating that frames act as adaptive mechanisms responding to change rather than static interpretative packages. The observed transitions across crises suggest a universal narrative structure adaptable to numerous frameworks. For crisis communication scholarship, our optimisation framework incorporates narrative and emotion alongside transparency, providing a model to explain public engagement variance across crisis types. The relevant stakeholders are numerous: media companies could utilise insights on frame change for more sophisticated narrative planning around crises; risk managers can optimise trust through strategic calibration of transparency; and policymakers can forecast information requirements from the public, including all stages of the crisis. It is useful for crisis communicators in all fields that during technological crises, problem-solution narratives tend to outperform other structural forms, and in public health emergencies, sequential narratives reign supreme. Our model demonstrates robust performance across crisis types, though limitations include potential geographic and cultural specificity of the identified patterns. The dataset, while comprehensive, primarily reflects Western media ecosystems, potentially limiting generalizability to other cultural contexts. Additionally, the focus on text analysis excludes visual framing elements that may significantly influence public perception during crises. Future research should examine cross-cultural variations in frame effectiveness and explore the application of these approaches to emerging crisis types. Integrating multimodal analysis to capture both textual and visual framing mechanisms would provide a more comprehensive understanding of crisis communication dynamics. Longitudinal studies tracking the relationship between framing strategies and public behavioral outcomes could further validate the optimization framework and refine its practical applications. The methodological approach developed here opens promising avenues for computational analysis of crisis narratives across disciplines. 6. Conclusion This research has developed a comprehensive framework for analyzing and optimizing media framing in crisis communication through advanced deep learning techniques. By examining large-scale media content across three distinct crisis events, we identified systematic patterns in frame evolution and developed an optimization framework integrating narrative structure, emotional factors, and transparency dimensions. The study makes significant knowledge contributions by demonstrating how computational methods can enhance understanding of crisis narratives, revealing non-linear relationships between transparency and trust, and identifying crisis-specific optimal communication strategies. Practical recommendations include dynamic frame adaptation as crises evolve, strategic calibration of transparency levels to maximize trust-building efficiency, and alignment of narrative structures with crisis types. While our approach demonstrates robust performance, limitations include potential cultural specificity and the exclusion of visual framing elements. Future research should explore cross-cultural variations in frame effectiveness, integrate multimodal analysis capturing both textual and visual elements, and develop real-time monitoring systems for crisis communication optimization. This research establishes a foundation for evidence-based crisis communication that can enhance public understanding and appropriate response during critical events. Ethical issue The author is aware of and complies with best practices in publication ethics, specifically with regard to authorship (avoidance of guest authorship), dual submission, manipulation of figures, competing interests, and compliance with policies on research ethics. The author adheres to publication requirements that the submitted work is original and has not been published elsewhere. 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