An Enhanced Ensemble Framework for Disaster Tweet Categorization

In the contemporary landscape, Twitter has evolved into an omnipresent platform, serving diverse functions ranging from expressing movie reviews to swiftly disseminating breaking news, notably in the crucial domain of disaster reporting. The ubiquity of smartphones empowers individuals to share immediate and firsthand disaster observations, while institutions like Disaster Relief Organizations and News Agencies strategically deploy Twitter for systematic and automated monitoring. This proactive strategy enables rapid global disaster identification and response, facilitating well-informed decision-making. Twitter stands as an indispensable tool in the lives of the general public, acting as a primary medium for both information dissemination and disaster reporting. The inherent challenge lies in distinguishing between informative and non-informative disaster announcements amid the vast array of tweets. This challenge is compounded by the sheer volume of information and the need for swift and accurate identification. To confront this challenge, sophisticated methodologies are employed, leveraging advanced technologies such as machine learning, deep learning, and ensemble approaches that integrate both modalities. Tweets are categorized into informative or non-informative classes using machine learning models. Additionally, deep learning models like BERT are employed for their ability to capture intricate contextual nuances in language. Leveraging ensemble learning techniques, which amalgamate the capabilities of various models, significantly improves the effectiveness of disaster monitoring on the Twitter platform. Ensemble learning, through methods like stacking and voting, helps in optimizing performance by considering diverse perspectives and mitigating the limitations of individual models. This comprehensive approach not only enhances public safety but also fortifies global disaster response capabilities, elevating the precision and efficiency of tweet classification. The integration of ensemble learning techniques reflects a strategic approach to finding optimal performance in differentiating between disaster-related tweets that convey information and those that do not on Twitter.