Collaborating with Journalists and AI: Leveraging Social Media Images for Enhanced Disaster Resilience and Recovery

Paper by Murthy Dhiraj et al: “Methods to meaningfully integrate journalists into crisis informatics remain lacking. We explored the feasibility of generating a real-time, priority-driven map of infrastructure damage during a natural disaster by strategically selecting journalist networks to identify sources of image-based infrastructure-damage data. Using the REST Twitter API, 1,000,522 tweets were collected from September 13-18, 2018, during and after Hurricane Florence made landfall in the United States. Tweets were classified by source (e.g., news organizations or citizen journalists), and 11,638 images were extracted. We utilized Google’s AutoML Vision software to successfully develop a machine learning image classification model to interpret this sample of images. As a result, 80% of our labeled data was used for training, 10% for validation, and 10% for testing. The model achieved an average precision of 90.6%, an average recall of 77.2%, and an F1 score of .834. In the future, establishing strategic networks of journalists ahead of disasters will reduce the time needed to identify disaster-response targets, thereby focusing relief and recovery efforts in real-time. This approach ultimately aims to save lives and mitigate harm…(More)”.