Evaluating Multi-label Classification of Incident-related Tweet

Microblogs are an important source of information in emergency management as lots of situational information is shared, both by citizens and ocial sources. It has been shown that incident-related information can be identified in the huge amount of available information using machine learning. Nevertheless, the currently used classification techniques only assign a single label to a micropost, resulting in a loss of important information that would be valuable for crisis management. With this paper we contribute the first in-depth analysis of multi-label classification of incident-related tweets. We present an approach assigning multiple labels to these messages, providing additional information about the situation at-hand. An evaluation shows that multi-label classification is applicable for detecting multiple labels with an exact match of 84.35%. Thus, it is a valuable means for classifying incident-related tweets. Furthermore, we show that correlation between labels can be taken into account for these kinds of classification tasks.