Evolution Analysis of Societal Risk Events by Risk Maps

Event evolution analysis which provides an effective approach to capture the main context of a story from explosive increased news texts has become the critical basis for many real applications, such as crisis and emergency management and decision making. Especially, the development of societal risk events which may cause some possible harm to society or individuals has been heavily concerned by both the government and the public. In order to capture the evolution and trends of societal risk events, this paper presents an improved algorithm based on the method of information maps. It contains an event-level cluster generation algorithm and an evaluation algorithm. The main work includes: 1) Word embedding representation is adopted and event-level clusters are chosen as nodes of the events evolution chains which may comprehensively present the underlying structure of events. Meanwhile, clusters that consist of risk-labeled events enable to illustrate how events evolve along the time with transitions of risks. 2) One real-world case, the event of “Chinese Red Cross”, is studied and a series of experiments are conducted. 3) An evaluation algorithm is proposed on the basis of indicators of map construction without massive human-annotated dataset. Our approach for event evolution analysis automatically generates a visual evolution of societal risk events, displaying a clear and structural picture of events development.