AI and journalistic networks: A synergistic approach to disaster damage surveillance.

This study investigates the potential for integrating AI and journalistic networks to create real-time, priority-driven maps of infrastructure damage during natural disasters. Focusing on Hurricane Florence in 2018, we collected over a million tweets using the REST Twitter API and extracted 11,638 images for analysis. Tweets were categorized by source, including news organizations and citizen journalists. We applied the OpenAI CLIP unsupervised machine learning model for image classification, splitting the data into 80 % for training, 10 % for validation, and 10 % for testing. The model achieved an average precision of 92 %, recall of 78 %, and an F1 score of 85 %. When compared to other models such as ViT and DeiT, which achieved F1 scores of 82.9 and 81.2, respectively, CLIP performed similarly but stood out due to its accessibility and zero-shot learning capabilities, making it ideal for rapid deployment in newsrooms and crisis scenarios. The framework's success was further demonstrated by cross-referencing model predictions with geotagged metadata and journalist sources, which linked damage locations with credible information. By leveraging this AI-based framework, journalists can significantly reduce the time needed to identify disaster-response targets, helping to focus relief and recovery efforts in real time. This approach enhances disaster data collection, analysis, and dissemination, ultimately saving lives and reducing harm by providing more efficient and accurate damage assessments. The study highlights how AI and journalistic networks can collaborate to improve crisis response efforts. [ABSTRACT FROM AUTHOR]