⚡ Quick Summary
This study explores the use of a hybrid CNN-LSTM model for health monitoring and crisis forecasting during the COVID-19 pandemic. The model effectively retrieves secure information from social media, achieving a precision of 63.74% and an F1-score of 71.66%, highlighting its potential in combating fake news.
🔍 Key Details
- 📊 Dataset: Publicly available dataset for model evaluation
- 🧩 Features used: Safe content from multiple social media sources
- ⚙️ Technology: Hybrid model combining CNN and LSTM
- 🏆 Performance: Precision 63.74%, Accuracy 59.33%, F1-score 71.66%, MCC 56.61%
🔑 Key Takeaways
- 📊 Hybrid model integrates CNN and LSTM for enhanced data retrieval.
- 💡 Educational awareness is crucial in combating misinformation during crises.
- 🤖 AI technologies can significantly improve the quality of information retrieved from social media.
- 🏆 Model performance shows promising results compared to traditional approaches.
- 🌍 Focus on COVID-19 highlights the relevance of the study in real-world health crises.
- 🛡️ Security against fake news is a primary goal of the research.
- 📈 Continuous improvement of the model is essential for future applications.
📚 Background
In today’s digital age, social networks play a pivotal role in disseminating information. However, this has led to an overwhelming volume of unsecured data, particularly during crises like the COVID-19 pandemic. The challenge lies in efficiently capturing safe and reliable information while minimizing the spread of fake news. This study addresses these challenges by leveraging advanced technologies.
🗒️ Study
The research conducted by Bouzidi and Boudries focuses on developing a hybrid CNN-LSTM model to monitor health and forecast crises. By analyzing data from various social media platforms, the study aims to retrieve secure and meaningful content, thereby enhancing the quality of information available during health emergencies.
📈 Results
The hybrid model demonstrated impressive performance metrics, achieving a precision of 63.74%, an accuracy of 59.33%, and an F1-score of 71.66%. The Matthews Correlation Coefficient (MCC) was recorded at 56.61%, indicating a strong capability in distinguishing between safe and unsafe content. These results were benchmarked against other existing approaches, showcasing the model’s effectiveness.
🌍 Impact and Implications
The implications of this study are significant, particularly in the context of health crises. By integrating artificial intelligence with social media technologies, the research paves the way for more reliable information retrieval. This can lead to better public awareness and response during emergencies, ultimately contributing to improved health outcomes and sustainability.
🔮 Conclusion
This study highlights the transformative potential of combining AI technologies with educational awareness in managing health crises. The promising results of the hybrid CNN-LSTM model suggest that such innovations can play a crucial role in enhancing the quality of information during critical times. Continued research and development in this area are essential for future advancements in health crisis management.
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Managing emergency crises using secure information through educational awareness: COVID-19 case study.
Abstract
Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources. Educational awareness is a fairly important tool and a constant reminder to do everything to avoid fake news. The hybrid model captures safe and meaningful features from multiple social media sources. This research study enables retrieval of qualitative and secure content and mainly effective security against fake news. The results are compared to other approaches thanks to a publicly available dataset, which shows a very satisfactory performance with a precision of 63.74%, an accuracy of 59.33%, an F1-score of 71.66% and Matthews Correlation Coefficient (MCC) with 56.61%. This study allows integrating social media technologies, and artificial intelligence to avoid fake news. The training is combined with educational awareness to always carefully retrieve safe pattern information from multiple social media sources while improving the CNN-LSTM-based alert model. Finally, the hybrid model is evaluated on the Coronavirus Disease 2019 (COVID-19) health crisis to obtain promising results compared to other approaches. This comparison shows extremely positive educational effects on reducing health crisis alerts in sustainability.
Author: [‘Bouzidi Z’, ‘Boudries A’]
Journal: Comput Biol Med
Citation: Bouzidi Z and Boudries A. Managing emergency crises using secure information through educational awareness: COVID-19 case study. Managing emergency crises using secure information through educational awareness: COVID-19 case study. 2025; 186:109620. doi: 10.1016/j.compbiomed.2024.109620