Abstract

Humanitarian and geopolitical crises (such as COVID-19) are frequently extra-national in scope. Technology, including applications of natural language processing and machine learning, can play a vital role in mitigating this burden, especially with availability and real-time analyses of social media. One such application is situation labeling, intuitively defined as the semi-automatic assignment of one or more actionable labels (such as food, medicine or water) from a controlled vocabulary to tweets or documents that become available in the aftermath of a crisis, such as an earthquake. Despite multiple advances, users of current situation labeling systems are often unwilling to trust these (and other machine learning) outputs without some provenance and visualization of results. This article describes an interactive visualization approach called SAVIZ that allows non-technical users to intuitively and interactively …

Date

March 19, 2026

Authors

Mayank Kejriwal, Peilin Zhou

Journal

Big Data and Social Media Analytics: Trending Applications

Pages

153-166

Publisher

Springer International Publishing