Crisis management knowledge from social media

Abstract

More and more crisis managers, crisis communicators and laypeople use Twitter and other social media to provide or seek crisis information. In this paper, we focus on retrospective conversion of human-safety related data to crisis management knowledge. First, we study how Twitter data can be classified into the seven categories of the United Nations Development Program Security Model (i.e., Food, Health, Politics, Economic, Personal, Community, and Environment). We conclude that these topic categories are applicable, and supplementing them with classification of individual authors into more generic sources of data (i.e., Official authorities, Media, and Laypeople) allows curating data and assessing crisis maturity. Second, we introduce automated classifiers, based on supervised learning and decision rules, for both tasks and evaluate their correctness. This evaluation uses two datasets collected during the crises of Queensland floods and NZ Earthquake in 2011. The topic classifier performs well in the major categories (i.e., 120--190 training instances) of Economic (F = 0.76) and Community (F = 0.67) while in the minor categories (i.e., 0--60 training instances) the results are more modest (F ≤ 0.41). The source classifier shows excellent results (F ≥ 0.83) in all categories.