Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media

• 成果类型: Article
• 署名作者: Johnson, Matthew; Murthy, Dhiraj; Robertson, Brett W.; Smith, William Roth; Stephens, Keri K.
• 署名单位: University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of South Carolina System; University of South Carolina Columbia; University of Tennessee System; University of Tennessee Knoxville; University of Texas System; University of Texas Austin
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2023.2172778
• 发表日期: 2023
• 页码: 163-182
• 关键词: Information diffusion; CRISIS

摘要

Social media platforms are increasingly used during disasters. In the United States, users often consider these platforms to be reliable news sources and they believe first responders will see what they publicly post. While having ways to request help during disasters might save lives, this information is difficult to find because non-relevant content on social media completely overshadows content reflective of who needs help. To resolve this issue, we develop a framework for classifying hurricane-related images that have been human-annotated. Our approach uses transfer learning and classifies each image using the VGG-16 convolutional neural network and multi-layer perceptron classifiers according to the urgency, relevance, and time period, in addition to the presence of damage and relief motifs. We find that our framework not only successfully functions as an accurate method for hurricane-related image classification but also that real-time classification of social media images using a small training set is possible.