A rapid-prototyping framework for extracting small-scale incident-related information in microblogs: Application of multi-label classification on tweets. (April 2016)

• Record Type: Journal Article
• Authors: Schulz, Axel; Loza Mencía, Eneldo; Schmidt, Benedikt
• Is Part Of: Information systems. Volume 57(2016)
• Page Start: 88
• Page End: 110
• Publication Date: 2016-04
• Subjects: Mining microblogs -- Disaster management -- Multi-label classification -- Incident type detection; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2015.10.010
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: Small scale-incidents such as car crashes or fires occur with high frequency and in sum involve more people and consume more money than large and infrequent incidents. Therefore, the support of small-scale incident management is of high importance. Microblogs are an important source of information to support incident management as important situational information is shared, both by citizens and official sources. While microblogs are already used to address large-scale incidents detecting small-scale incident-related information was not satisfyingly possible so far. In this paper we investigate small-scale incident reporting behavior with microblogs. Based on our findings, we present an easily extensible rapid prototyping framework for information extraction of incident-related tweets. The framework enables the precise identification and extraction of information relevant for emergency management. We evaluate the rapid prototyping capabilities and usefulness of the framework by implementing the multi-label classification of tweets related to small-scale incidents. An evaluation shows that our approach is applicable for detecting multiple labels with an match rate of 84.35%.