During crisis situations, observations of other people’s behaviors often play an essential role in a person’s decision-making. For example, a person might evacuate before a hurricane only if everyone else in the neighborhood does so. Conversely, a person might stay if no one else is leaving. Such observations are called social cues. Social cues are important for understanding people’s response to crises, so recognizing them can help inform the decisions of government officials and emergency responders. In this paper, we propose the first NLP task to categorize social cues in social media posts during crisis situations. We introduce a manually annotated dataset of 6,000 tweets, labeled with respect to eight social cue categories. We also present experimental results of several classification models, which show that some types of social cues can be recognized reasonably well, but overall this task is challenging for NLP systems. We further present error analyses to identify specific types of mistakes and promising directions for future research on this task.

When a hazard such as a hurricane threatens, people are forced to make a wide variety of decisions, and the information they receive and produce can influence their own and others’ actions. As social media grows more popular, an increasing number of people are using social media platforms to obtain and share information about approaching threats and discuss their interpretations of the threat and their protective decisions. This work aims to improve understanding of natural disasters through social media and provide an annotation scheme to identify themes in user’s social media behavior and facilitate efforts in supervised machine learning. To that end, this work has three contributions: (1) the creation of an annotation scheme to consistently identify hazard-related themes in Twitter, (2) an overview of agreement rates and difficulties in identifying annotation categories, and (3) a public release of both the dataset and guidelines developed from this scheme.