Abstract
One of the key factors influencing how people react to and behave during a crisis is their digital or non-digital social network, and the information they receive through this network. Publicly available online social media sites make it possible for crisis management organizations to use some of these experiences as input for their decision-making. We describe a methodology for collecting a large number of relevant tweets and annotating them with emotional labels. This methodology has been used for creating a training dataset consisting of manually annotated tweets from the Sandy hurricane. Those tweets have been utilized for building machine learning classifiers able to automatically classify new tweets. Results show that a support vector machine achieves the best results with about 60% accuracy on the multi-classification problem. This classifier has been used as a basis for constructing a decision support tool where emotional trends are visualized. To evaluate the tool, it has been successfully integrated with a pan-European alerting system, and demonstrated as part of a crisis management concept during a public event involving relevant stakeholders.
Highlights
During crises, enormous amounts of user generated content, including tweets, blog posts, and forum messages, are created, as documented in a number of recent publications [1,2,3,4,5,6]
By comparing the results to those achieved when using a rule-based classifier we show that the used machine learning algorithms have been able to generalize from the training data and can be used for classification of new, previously unseen, crisis tweets
The concept was well received, considered novel, and makes it possible for crisis management organizations to use a new type of input for their decision-making
Summary
Enormous amounts of user generated content, including tweets, blog posts, and forum messages, are created, as documented in a number of recent publications [1,2,3,4,5,6]. The flood of information that is broadcast is infeasible for people to effectively extract information from, organize, make sense of, and act upon without appropriate computer support [6] For this reason, several researchers and practitioners are interested in developing systems for social of interest. In the presence of relevant stakeholders representing politics, industry, end users, and research communities, this system was successfully demonstrated as a cohesive system during a public event. As part of this event, the classification of social media posts was used to visualize emotional trend statistics for the purpose of demonstrating the idea of using social media input for informing crisis management decisions. The concept was well received, considered novel, and makes it possible for crisis management organizations to use a new type of input for their decision-making
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