Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Twitter under crisis: can we trust what we RT?
Proceedings of the First Workshop on Social Media Analytics
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
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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.