







































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 

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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. 



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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 



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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 



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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. 

 
 



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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 

 

 

 

 



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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 

 
 



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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. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the author. 

Conflict of interest 

The author declares no potential conflict of interest. 

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