Crisis Communication Patterns in Social Media during Hurricane Sandy

Hurricane Sandy was one of the deadliest and costliest of hurricanes over the past few decades. Many states experienced significant power outage, however many people used social media to communicate while having limited or no access to traditional information sources. In this study, we explored the evolution of various communication patterns using machine learning techniques and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ~52M tweets coming from ~13M users between October 14, 2012 and November 12, 2012. We run topic model on ~763K tweets from top 4,029 most frequent users who tweeted about Sandy at least 100 times. We identified 250 well-defined communication patterns based on perplexity. Conversations of most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. We also present each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real time user needs in future crises

A data-driven approach towards a realistic and generic crowd simulation framework

Jacob Sinclair studied and developed a data-driven approach towards a realistic and generic crowd simulation framework. He found that by using virtual reality and questionnaires, we can gather all types of real world data. He also found that an AI framework developed using all types of data can produce similar results to the real world. This AI framework has the potential to be used to improve areas such as emergency management and response, traffic control, building design, video games, etc

A Coupled SFM-ASCRIBE Model To Investigate the Influence of Emotions and Collective Behavior in Homogeneous and Heterogeneous Crowds

The understanding of crowd behavior dynamics holds immense significance in ensuring public safety across a range of situations, including emergency evacuations and large-scale events. Our research focuses on two primary objectives: investigating the impact of emotions on crowd movement and gaining valuable insights into collective behavior within crowds. To achieve this, we present a coupled model, incorporating an enhanced ASCRIBE model with an agent displacement model. We introduce heterogeneity into our model by incorporating specific mobility laws for different categories of panicked crowds, considering the influence of emotions on both speed and direction. Through numerical simulations, we analyze the model's parameters, observe the behavior of uniform crowds, and explore the collective dynamics within diverse crowds. By conducting comprehensive simulations and analyses, the findings from this study can contribute to the development of more effective crowd management strategies and emergency evacuation protocols

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia

'Catastrophic Failure' Theories and Disaster Journalism: Evaluating Media Explanations of the Black Saturday Bushfires

In recent decades, academic researchers of natural disasters and emergency management have developed a canonical literature on 'catastrophe failure' theories such as disaster responses from from US emergency management services (Drabek, 2010; Quarantelli, 1998) and the Three Mile Island nuclear power plant (Perrow, 1999). This article examines six influential theories from this field in an attempt to explore why Victoria's disaster and emergency management response systems failed during Australia's Black Saturday bushfires. How well, if at all, are these theories understood by journalists, disaster and emergency management planners, and policy-makers? On examining the Country Fire Authority's response to the fires, as well as the media's reportage of them, we use the 2009 Black Saturday bushfires as a theory-testing case study of failures in emergency management, preparation and planning. We conclude that journalists can learn important lessons from academics' specialist knowledge about disaster and emergency management responses

People behaviors in crisis situations : Three modeling propositions

International audienceWarnings can help to prevent damages and harm if they are issued timely and provide information that help responders and population to adequately prepare for the disaster to come. Today, there are many indicator and sensor systems that are designed to reduce disaster risks. These systems have proved to be effective. Unfortunately, as all systems including human beings, a part of unpredictable remains. Indeed, each person behaves differently when a problem arises. In this paper, we focus on people behaviors in crisis situations: from the definition of factors that impact human behavior to the integration of these behaviors, with three different modeling propositions, into a warning system in order to have more and more efficient crisis management systems