PROJECT TITLE :
Network-Based Modeling for Characterizing Human Collective Behaviors During Extreme Events - 2017
Modeling and predicting human dynamic behaviors within the face of stress and uncertainty can facilitate perceive and prevent potential irrational behavior, like panic shopping for or evacuations, in the wake of utmost events. However, in terms of the types of events and also the distinct human psychological factors, like risk perception (RP) and emotional intensity (EI), human dynamic behaviors exhibit heterogeneous spatiotemporal characteristics. For example, we will observe different collective responses to the identical events by folks in several regions, with distinct trends unfolding over time. To provide a computational suggests that for understanding the spatiotemporal characteristics of human behaviors during completely different varieties of extreme events, here we gift a network-based mostly model that permits us to characterize dynamic behaviors. This model assumes the attitude of a dynamic system, whose behavior is driven by human psychological factors and by the network structure of interactions among individuals. By creating use of the obtainable information from Twitter and GoogleTrends, we have a tendency to conduct a case study of human dynamic behavioral and emotional responses to the Japanese earthquake in 2011 so as to look at the effectiveness of our proposed model. With this model, we further assess the impacts of a happening by evaluating the interrelationships of human RP and levels of EI in terms of observed collective behaviors. The results demonstrate that human behaviors are subjected to personal observations, experiences, and interactions, that can doubtless alter perceptions and magnify the impacts of an event.
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