Message Information Research Articles

Arguing with numbers in social media based COVID-19 debates: A study of the argumentative functions of visual graphs

This study engages with a growing body of literature on numerical communication in COVID-19, which suggests that numbers and numerical measurements can be manipulated to shape governmental policy and public health measures. It uniquely focuses on numerical graphs generated through professional open-resource visualization tools but used by laypersons in social media debates. By combining theories of multimodality and linguistic theories of argumentativity, we analyzed Twitter interactions on COVID-19 policy that occurred on the Twitter account of a leading public health figure in Scotland. Our fine-grained qualitative analysis shows that while language choices are essential for establishing the argumentative context for graphs, choices about how to encode visual information, such as the placement of elements in the composition, can also provide information about the communicator’s intention. To understand the argumentative purpose of the graphs, the interlocutor needs to grasp not only how information is packaged in the multimodal message but also the argumentative agenda of the speaker/writer, which is encoded in their multimodal message and other contextual information.

Influencing Canadian young adults to receive additional COVID-19 vaccination shots: the efficacy of brief video interventions focusing on altruism and individualism.

Younger adults, aged 18-39 years, exhibit low COVID-19 additional vaccine (i.e., vaccination beyond the original 2-dose series) uptake recommended in Canada. No study has examined how altruistic and individualistic messaging can influence COVID-19 additional dose intentions. The present study aimed to estimate the efficacy of altruism and individualism-based videos on vaccine intentions and to explore the multivariable associations between vaccine related individual psychosocial factors and intention to receive the COVID-19 vaccine. Using a web-based survey in a three-arm, pre-post randomized control trial design, we recruited Canadians aged 18-39 years in both English and French. Participants were randomly allocated in a 1:1:1 ratio to receive the active control (COVID-19 general information), control + altruism or control + altruism + individualism. The video interventions were developed with a media company, based on results of a focus group study conducted previously. The measurement of COVID-19 additional dosage intentions before and after completing the interventions was informed by the multistage Precaution Adoption Process Model. The McNemar Chi-square was used to evaluate within-group changes, and the Pearson Chi-square test was used to evaluate between-group changes post-intervention. The measurement of various psychosocial factors was informed by use of validated scale and self-report questions. We employed a generalized Structural Equation Model to evaluate the associations between COVID-19 vaccine intentions and the psychosocial factors. Analyses were performed on 3,431 participants (control: n = 1,149, control + altruism: n = 1,142, control + altruism + individualism: n = 1,140). Within-group results showed that participants transitioned significantly in all three groups in the direction of higher intentions for receiving additional COVID-19 vaccine doses. The between-group differences in post intervention vaccine intentions were not significant. We found that psychosocial factors that include, collectivism, intellectual humility, intolerance to uncertainty, religiosity, identifying as gender diverse, and being indigenous were associated with higher vaccine intentions, whereas pandemic fatigue was associated with lower vaccine intentions. Our study highlighted that a short video that includes altruism and individualism messaging or general COVID-19 information can increase intentions to vaccine among young adults. Furthermore, we gained a comprehensive understanding of various psychosocial factors that influence ongoing COVID-19 vaccination. Our findings can be used to influence public health messaging around COVID-19 vaccination.

Social Trust Confirmation-Based Selfish Node Detection Algorithm in Socially Aware Networks

Nodes in socially aware networks (SANs) may act selfishly on individual bases due to resource constraints and socially selfish behavior arising from the social preferences of nodes. In response to such selfish behaviors exhibited by nodes, this paper proposes a social trust confirmation-based selfish node detection algorithm (STCDA). This algorithm first utilizes a subjective forwarding willingness detection mechanism to discern selfishness. If a node’s energy is insufficient or its message rejection rate is too high—that is, the node cannot or is unwilling to forward messages—it indicates that the node is selfish. Otherwise, it is evaluated more thoroughly through the node’s social trust detection mechanisms. It calculates the social trust level of nodes based on the benefits of forwarding messages, thereby distinguishing between individually selfish nodes and socially selfish nodes in the network. If further evaluation is needed, the final judgment will be made using the message confirmation feedback detection mechanism. This checks the message information forwarded by nodes in the network. If nodes fail to forward messages after receiving them—excluding reasons such as message expiration or temporary insufficient cache space—it indicates that the nodes are selfish. Results from experimental simulations show that this algorithm performs better than traditional algorithms. Under conditions of 80% selfish nodes, a message TTL of 300 min, and 10 MB of cache space, it improves the message delivery rate by 5.87% and reduces the average delay by 6.2% compared to the existing comprehensive confirmation-based selfish node detection algorithm.

Sistem Informasi Bimbingan Tugas Akhir Mahasiswa menggunakan Model SDLC Berbasis Iconix Process

The final thesis assignment plays a crucial role in enabling students to meet the graduation requirements from college. However, the process of scheduling guidance for the final assignment between students and lecturers still relies on several common applications such as WhatsApp or email, which are not specifically designed for this purpose. The accumulation of incoming messages and various types of message information poses a challenge in the guidance process, leading to missed messages between students and lecturers and a lack of recorded information regarding the history of the process and guidance materials. These are some of the current issues. This paper aims to evaluate the functionality, quality, and reliability of the system by conducting black box testing on the application developed for the student final project guidance information system. This application uses the Iconix process-based SDLC (system development life cycle) model, covering student and lecturer profile information, guidance information, proposal submission, progress of the final thesis assignment, meeting schedule, guidance material, discussion forum, survey evaluation, and contact information. The SDLC model is employed in this research because it can effectively and efficiently achieve project targets, enhance software quality standards, and assist in better risk management and adaptation to change. The model comprises planning, needs analysis, design using the Iconix process, implementation, system testing, and maintenance. The Iconix process is utilized for system design modeling and analysis. Black box testing is performed on the system to verify that the system’s functional requirements are operating correctly. The findings of this research can serve as a control for management in the service and administration of final assignment guidance in higher education.

Extracting geo-references from social media text using bi-long short term memory networks

<p>The social media data provides great source of information about global and local events, with millions of users. More precisely, the fact that brief messages are practical and are highly popular. Many recent studies have been motivated to estimate the location of the events identified by tracking posts in social media text messages. It might be difficult to extract location data and estimate the location of an event while maintaining a sufficient level of situation awareness, particularly in disaster situations like fires or traffic accidents. In this presented work we proposed an approach to identify geo-references in the text messages. We used bi-directional long short term memory (LSTM) neural networks to extract location information in the text messages. The results show that applying Bi-LSTM on dataset gives high level accuracy after fine-tuning (up to 10 epochs). The testing results show that accuracy achieved is 0.98 and 0.076 loss value. This proves that the proposed methodology is better than the previous conditional random field (CRF)-based approaches.</p>

Pregnant mother’s intention to use mobile phone-based messaging interventions for improving maternal and newborn health practices in Jimma Zone, Ethiopia

BackgroundMobile phone-based messaging for maternal education has shown promising outcomes in promoting maternal and child healthcare in low- and middle-income countries, where there is poor utilization of healthcare services. However, the success of a mobile phone messaging-based intervention depends on identifying and addressing the underlying factors that determine its utilization before implementation.ObjectiveTo assess pregnant mothers’ intention to use a mobile phone-based messaging intervention to improve maternal and newborn health in Jimma Zone, Ethiopia.MethodThis study employed a cross-sectional study design among randomly selected pregnant mothers residing in six primary health care units that were selected from three districts of Jimma Zone, Ethiopia. Data were collected using an interviewer-administered structured questionnaire. Descriptive statistics such as frequencies, percentages, mean, and standard deviation were calculated and presented using tables. Multivariable linear regression analysis was used to identify predictors of intention. Standardized regression coefficients were used to understand the effect of the independent variables and outcome variable. A 95% confidence interval and a p-value of < 0.05 were considered significant.ResultTwo hundred seventy-nine pregnant mothers participated in this study, resulting in a 98.9% response rate. Almost all (98.9%) respondents preferred the Afan Oromo language, 41.9% of respondents preferred the afternoon and 48.4% preferred receiving health information as text messages once daily. Having family members who can share SMS information (β = 0.098, 95% CI: (0.279—1.867), perceived usefulness (β = 0.283, 95% CI: (0.143—0.341), perceived easiness (β = 0.209, 95% CI: (0.054—0.392), perceived acceptability (β = 0.158, 95% CI: (0.007—0.178) and perceived feasibility (β = 0.186, 95% CI: (0.057—0.284) were found to have a positive significant association with the intention to use mobile phone-based messaging for maternal and newborn health.ConclusionThe majority of respondents preferred receiving mobile phone-based messaging on MNH in the Afan Oromo language, during the afternoon, and once daily. Having family members who share text message information, perceived usefulness, perceived easiness, perceived acceptability, and feasibility were significantly associated with intention. These findings highlight the importance of considering user preferences and determinants of intention when designing mobile phone-based messaging.

Antenna and attitude modeling of modernized GLONASS satellites

Next to the legacy frequency division multiple access (FDMA) signals in the L1 and L2 band, modernized GLONASS satellites of the M+, K1, and K2 generation also transmit new code division multiple access (CDMA) signals on L3, and, in part, the L1 and L2 frequencies. Depending on the specific satellite platform, either a common antenna or two distinct antennas are used for the individual signals. As a novel feature, the new CDMA navigation messages provide information on the antenna phase center locations as well as the spacecraft orientation during rate-limited noon and midnight turns. We establish a comprehensive set of phase center positions for the individual GLONASS antennas from manufacturer data and information in the CDMA navigation messages, inferred from the comparison of FDMA and CDMA broadcast ephemerides, and obtained from antenna baseline estimates using a triple-frequency carrier phase combination. Based on these, reference values for the signal-specific PCOs of each block of modernized GLONASS spacecraft are derived that may be used to extend the current antenna model of the International GNSS Service (IGS) to all frequency bands. Complementary to the analysis of antenna phase center information, the new CDMA attitude model for GLONASS-K satellites is reviewed. Algorithms for computing relevant parameters of the rate-limited yaw slews including the ramp-up and -down phase based on the Sun/orbit geometry are derived that enable a concise attitude modeling in GLONASS orbit determination and precise point positioning. The model is validated through comparison with yaw angle estimates derived from triple-frequency observations showing consistency at the 1° level for the K2 satellite. On the other hand, unexpected accelerations during noon turns of GLONASS-M+ are identified, which may cause short-term yaw angle deviations of up to 20° relative to attitude models in common use within the IGS.

Environmental or Personal Benefit? The Role of Message Appeal and Information Type in Destination Social Media Advertisements

Destinations are increasingly focusing on sustainability awareness through tourism campaigns, but effective communication remains challenging. This research, rooted in consumption value theory, delves into the influence of message appeal (environmental vs. personal benefit) and information type (objective vs. subjective) on tourists’ behavioral intentions through two online experiments. Study 1 demonstrates the positive impact of environmental benefit message appeals on behavioral intentions toward destinations, both directly and indirectly, by enhancing destination brand quality and perceived green value. Study 2 validates these findings and extends the analysis to include click-through behavior as a proxy for real behavior. Our research emphasizes the importance of environmental benefits alongside objective information in destinations’ sustainability messages within social media advertisements. This approach serves to bolster destination brand quality, perceived green value, and ultimately, positive behavioral intentions.

Effectiveness of Message Framing in Changing Restaurant Diners’ Plant-Based Meat Consumption

Drawing on regulatory fit theory, construal level theory, and means-end theory, this research examined the effects of message framing, message information, and message description on restaurant diners’ consumption intentions (CI) and the amount more they would be willing to pay ($WTP) for a plant-based diet. The study employed a mixed between-within-group methodology with a micro-longitudinal 2 (gain vs. loss framing) × 2 (health vs. environment information) × 2 (attribute-based vs. benefit-based description) scenario-based experimental design. An analysis of 361 survey responses revealed that health information should be conveyed through gain-framed messages with benefit-based descriptions, whereas environment information should be conveyed through loss-framed messages with attribute-based descriptions. These results enriched our understanding of diners’ attitudes towards plant-based meat consumption. The findings lay a theoretical foundation for future studies and present practical implications for the hospitality industry.

Development of Pidgin English Hate Speech Classification System for Social Media

With the widespread use of social media, people from all walks of life—individuals, friends, family, public and private organizations, business communities, states, and entire nations—are exchanging information in various formats, including text, messages, audio, video, cartons, and pictures. Social media also facilitates the distribution and propagation of hate speech, despite the immense benefits of knowledge sharing through these platforms. The purpose of this work was to construct a text-based, Pidgin English hate speech classification system (HSCS) in social media, taking into account the alarming rate at which hate speech is shared and propagated on social media, as well as the negative effects of hate speech on society. We used text data sets in Pidgin English that were taken from Twitter and Facebook (3,153). To train the Support Vector Machine (SVM) text classifier to identify hate speech in Pidgin English, 70% of the Pidgin English data set was annotated. The SVM classifier&amp;apos;s performance was tested and assessed using the remaining thirty percent of the Pidgin English text data set. The test set findings&amp;apos; confusion matrix, as determined by the HSCS performance evaluation, was 62.04%, 64.42%, 0.7541, 0.6947, and 0.64 in terms of accuracy, precision, recall, F1-score, and Receiver Operating Characteristics (ROC) curve. When HSCS was compared to other Machine Learning (ML) classifiers, such as Logistic Regression (LR), Random Forest (RF), and Naive Bayes, the results showed that LR had accuracy and precision of 61.51% and 63.89%, RF had 54.88% and 50.65%, and Naive Bayes had 61.51% and 63.89%.

Bridging gaps in COVID-19 vaccine knowledge: Effects of multimodal narratives on message elaboration and recall across science literacy levels

ObjectiveThe study examines the effects of message modalities (visual vs. textual) and rhetorical approaches (narrative vs. expository information) on the elaboration and recall of COVID-19 vaccine information among individuals with diverse levels of science literacy. MethodsWe recruited 328 U.S. adults who were not fully vaccinated against COVID-19 and measured their message elaboration and information recall following exposure to a comic strip, an infographic, a written story, and a short article pertaining to the safety of COVID-19 vaccines. ResultsResults indicated no significant difference in levels of message elaboration and information recall among the four treatment groups. However, exposure to narratives mitigated the gap in message elaboration, whereas exposure to visual information reduced the disparity in information recall among individuals with varying levels of science literacy. ConclusionNarratives can serve as a powerful tool for engaging individuals with lower levels of science literacy, as well as reducing the dependence on scientific knowledge for information processing among those with higher literacy levels. Furthermore, the inclusion of visuals in vaccine messages can augment their effectiveness in fostering shared knowledge among audiences with varying levels of science literacy.

A Fast Time Synchronization Method for Large Scale LEO Satellite Networks Based on A Bionic Algorithm

A fast time synchronization method for large-scale LEO satellite networks based on a bionic algorithm is proposed. Because the inter-satellite links are continuously established and interrupted due to the relative motion of the satellites, the topology of the LEO satellite networks is time varying. Firstly, according to the ephemeris information in navigation messages, a connection table which records the connections between satellites is generated. Then, based on the connection table, the current satellite network topology is calculated and generated. Furthermore, a bionic algorithm is used to select some satellites as time source nodes and calculate the hierarchy of the clock transmission tree. By taking the minimum level of the time transmission tree as the optimization objective, the time source nodes and the clock stratums of the whole satellite networks are obtained. Finally, the onboard computational center broadcasts the time layer table to all the satellites in the LEO satellite networks and the time synchronization links can be established or recovered fast.

Influence of online searches for campaign messages on voting behaviour in Ghana

This study ascertained the influence of political party campaign message information search on the behaviour of voters in Ghana. The researchers however conducted this study by applying the perspectives of consumer behaviour to voter behavior with an objective to examine how the behaviour of consumers (voters) is influenced by the search for information on election campaign messages in their decision-making process. The examination used quantitative research to determine the relationship between voting behaviour and political campaign with the cross-sectional survey deployed to collect data from 7203 voters in Accra. These voters were selected through random sampling and the data was collected with questionnaires. The study measured voter (consumer) behaviour from three dimensions namely psychological, social and personal behaviour. The Structural Equation Model (SEM) in Amos was used for the analysis to establish the relationship between the variables. The study revealed that significant positive relationship between campaign message information search and voter psychological behaviour. Likewise, information searches on campaign messages significantly influence voters' social and personal behaviour. The study concluded that the behaviour of voters is influenced by their search for content or details of political campaign messages. The study concludes that there are opportunities for political marketing and change in voter behaviour. Still, sufficient attention should be given to developing and deploying a consumer behaviour model that recognises the challenges and changes with political-marketing campaigning for vote in Ghana Political parties/candidates should make available adequate information on their campaign promises and manifestos to voters to influence their behaviour towards winning their votes. This study has contributed significantly to the knowledge of literature in the field of political marketing. Nonetheless, further studies should be conducted in other jurisdictions other than Ghana to validate the model or test the hypotheses.

Fake News Detection System Using Machine Learning and Deep Learning

The rise of fake news in today's age of mass media poses a serious threat to the credibility of the media and public discourse [1]. This project focuses on developing effective and robust solutions to detect fake news by integrating multiple machine learning algorithms. This project uses a variety of techniques, including natural language processing (NLP), clustering, classification algorithms and deep learning, to identify the content of patterns that indicate error messages. The project first explores NLP algorithms to extract content from news media content. These features allow the system to understand and classify the language used, helping to identify language patterns associated with fake news. Additionally, emotional analysis plays an important role in visualizing the tone and content of the text, further increasing the accuracy of the findings. The integration method is used to combine various machine learning models. By combining the performance of various algorithms, the system aims to overcome individual limitations and provide a more comprehensive and effective evaluation of media content. This approach helps increase the power of fake news detection and adapts to different fraud strategies. The project also includes deep learning algorithms, especially neural networks, to capture relationships in data. The use of deep learning ensures that the system can recognize nuances in the language and clarifies the complex information of the error message. Continuous updating and reworking of machine learning models is an important part of the process, allowing the system to constantly adapt to fraud strategies [2]. The aim of fake news detection system is to create a well-informed, weak search for fake news that can help create greater awareness and public approval. Key Words: Fake news detection, Machine learning techniques, Advanced algorithms, Classification algorithms, Deep learning, Feature extraction, Model training, Natural language processing

Convergence of Communicative Components in Infographics

The purpose of the research is to establish the presence or absence of the phenomenon of convergence between visual components formed based on different ways of encoding information in infographic messages. Particular attention is focused on those cases when the above-mentioned differently coded elements can provide a synergistic effect or supplement and enrich the content through the interaction of different meanings, each of which is presented in the most optimal way that does not imitate the other. Research methods are analytical, logical-evaluative, systematic approach and method of structural-compositional analysis. The scientific novelty consists in proving the presence of the effect of convergence of meanings in infographics formed based on different codes and different communicative elements. It was revealed that infographic messages can use up to seven different ways of visual presentation of information differing in stylistic and technical-graphic properties. At the same time, it has been proved that minimising the number of different style components leads to the formation of the most effective visual messages. Such messages are better remembered and remain in the minds of recipients longer. Conclusions. The use of communicative elements with different technical, graphic and stylistic properties outside infographic messages, in most cases, leads to the destruction of meaningful unity and loss of consumer interest in the content of the message. On the contrary, in infographics, the structural organisation of information, compositional zoning, and the logical links’ presence between different groups of visual-symbolic forms ensure the integrity of information impact. However, it has been proved that such integrity requires either the preservation of a single optical centre of content or well-defined levels of content with accentuated visual transitions between them.

Application of Machine Learning Algorithms for Carpal Tunnel Syndrome

Introduction: Median nerve entrapment is commonly related to alterations in the anatomy of the surrounding tissues in the carpal tunnel. Carpal tunnel syndrome (CTS) is the most prevalent form of peripheral entrapment neuropathy. Machine learning (ML) is utilized in a variety of fields. After conferring with a physician, ML enables doctors to conduct the necessary examinations and make an early diagnosis. Methods based on artificial intelligence have the potential to be utilized in clinical practice as a supplementary instrument for accurate evaluation of median nerve entrapment. Despite the rise in ML-based medical research, median nerve entrapment has received less attention. The purpose of this study was to evaluate the performance of classification approaches with ML algorithms in CTS patients utilizing electromyography test data from patients exhibiting varied CTS symptoms and indications. Materials and Methods: Our study includes message and demographic information derived from the electromyography results of 315 individuals. In classification procedures, the logistic regression, support vector machine (SVM), K-nearest neighbor, and naïve Bayes algorithms from ML techniques were utilized. Results: As a result of the classification, performance values for accuracy, precision, sensitivity, specificity, and F1-score were obtained. As a result of our research, the SVM algorithm achieved a 96% success rate. Conclusion: ML algorithms are an emerging method of analysis. The diagnosis and treatment of diseases are gradually gained by clinicians through observation and experience. Therefore, categorization systems can aid in the accurate and timely diagnosis of median nerve entrapment by clinicians.

Sustainable threads: analyzing the impact of corporate social performance, CSR message authenticity and information transparency on purchase intentions

With the deepening exploration of the study on corporate social responsibility (CSR), CSR has progressively gained significance for enterprises. This study aims to analyze the impact of fashion companies’ information disclosure on the intention of Chinese consumers who purchase clothing by providing information related to the environment, suppliers, manufacturing processes, and pricing. An experimental study was conducted using stimuli on 412 consumers in Shandong Province, China. The research results are as follows. First, it was found that consumers’ perception of Corporate Social Performance (CSP) has a positive effect on the authenticity of Corporate Social Responsibility (CSR) message and consumers’ subjective norms. Second, CSR message authenticity and consumer subjective norms mediate the perception of CSP in the process of leading to purchasing activities. Third, consumers perceive the authenticity of a company’s CSR activities according to the degree of transparent information disclosure of the cost and manufacturing process of its products. This study shows that the CSR information provided by the company is authentic depending on the degree of CSR information transparency of the company, and that it leads to a purchase intention when consumers judge that the company’s CSR activities are authentic. Accordingly, it is suggested that many fashion companies’ CSR activities lead to product purchases when they are continuously conducted on environmental, social problems, and transparency in the production process.

Whisper-Steg: Unveil the Hidden

Whisper-Steg represents a ground- breaking project at the intersection of secure messaging and advanced information concealment techniques. This innovative chat application transcends traditional boundaries by seamlessly integrating the art of steganography, offering users a novel and secure platform for communication. The project not only facilitates text and image sharing but introduces an ingenious Image Steganography feature, allowing users to embed confidential information within images. Incorporating an acknowledgment mechanism akin to popular messaging applications like WhatsApp, Whisper- Steg prioritizes the reliability of communication. This feature ensures that users receive confirmation of message delivery, enhancing the overall user experience. The project dynamically generates cover images based on the size of hidden messages, employing AI algorithms to optimize concealment. This not only enhances the security of the stenographic process but also underscores the project's commitment to leveraging cutting-edge technologies for user benefit. In an era where privacy concerns are paramount, Whisper-Steg embraces steganography and AI-driven solutions and stands as a testament to technological innovation, reshaping the landscape of secure digital communication. Key Words: Whisper-Steg, Steganography, chat application

Can information intervention improve dietary quality? Evidence from a randomized controlled trial in rural China

Nutrition education interventions are widely used globally but with mixed results. We conducted a randomized controlled trial to examine the effects of a nutrition education and a precise message (awareness-raising) intervention on nutrition knowledge and dietary quality of households in rural China. Treatment groups first received a lecture on the Chinese Food Guide Pagoda, and then all family members were measured for height and weight and informed of household overweight status. We analyzed 358 households before and after this intervention. Participants in the treatment group increased their dietary knowledge by 6% and improved their dietary quality by 8% after the intervention. The intervention effects were stronger for households with more than 25% and 50% of overweight people. Our study provides evidence that an intervention based on general nutrition information and delivery of a precise message to households can effectively improve dietary quality. Our findings inform the food policy debate on whether nutrition information interventions are effective. Furthermore, to the best of our knowledge, this is the first study to evaluate the effectiveness of a nutrition information intervention to improve dietary quality in rural China.

XR4DRAMA a knowledge-based system for disaster management and media planning

Abstract In the previous two decades, Knowledge Graphs (KGs) have evolved, inspiring developers to build ever-more context-related KGs. Because of this development, Artificial Intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, we introduce the XR4DRAMA framework. The KG of the XR4DRAMA framework can represent data for media preparation and disaster management. More specifically, the KG of the XR4DRAMA framework can represent information about: (a) Observations and Events (e.g., data collection of biometric sensors, information in photos and text messages), (b) Spatio-temporal (e.g., highlighted locations and timestamps), (c) Mitigation and response plans in crisis (e.g., first responder teams). In addition, we provide a mechanism that allows Points of Interest (POI) to be created or updated based on videos, photos, and text messages sent by users. For improved disaster management and media coverage of a location, POI serve as markers to journalists and first responders. A task creation mechanism is also provided for the disaster management scenario with the XR4DRAMA framework, which indicates to first responders and citizens what tasks need to be performed in case of an emergency. Finally, the XR4DRAMA framework has a danger zone creation mechanism. Danger zones are regions in a map that are considered as dangerous for citizens and first responders during a disaster management scenario and are annotated by a severity score. The last two mechanisms are based on a Decision Support System (DSS).