In recent years, the concept of human-in-the-loop has been utilized to support environment sensing. Along with
Internet of Things
) and wearable computing technologies, connecting people and devices to the internet provides a significant advantage for real-time emergency management. For development of context-aware applications, it is important to utilize higher-level semantic information, such as human activity, social emotions, and human behaviors for event monitoring. Therefore, human users may become part of sensor networks by using mobile devices and social media to report local information around them. In this work, we mainly focus on the use of social messages spreading by human users to model the real-world events, in order to incorporate human sensors into event contexts for situational awareness. First, our algorithm computes the energy of each collected event messages, and then encapsulates ranked temporal, spatial and topical keywords into a structured node, which could reinforce the alert collected from physical nodes. The experimental results show that the proposed approach is able to extract essential entities of events for incorporating human sensors into event contexts for event prevention and risk management.