Abstract

Valuable and timely information about crisis situations such as natural disasters, can be rapidly obtained from user-generated content in social media. This has created an emergent research field that has focused mostly on the problem of filtering and classifying potentially relevant messages during emergency situations. However, we believe important insight can be gained from studying online communications during disasters at a more comprehensive level. In this sense, a higher-level analysis could allow us to understand if there are collective patterns associated to certain characteristics of events. Following this motivation, we present a novel comparative analysis of 41 real-world crisis events. This analysis is based on textual and linguistic features of social media messages shared during these crises. For our comparison we considered hazard categories (i.e., human-induced and natural crises) as well as subcategories (i.e., intentional, accidental and so forth). Among other things, our results show that using only a small set of textual features, we can differentiate among types of events with 75% accuracy. Indicating that there are clear patterns in how people react to different extreme situations, depending on, for example, whether the event was triggered by natural causes or by human action. These findings have implications from a crisis response perspective, as they will allow experts to foresee patterns in emerging situations, even if there is no prior experience with an event of such characteristics.