Model of Forecasting the Social News Events on the Basis of Stochastic Dynamics Methods
1 Moscow Technological University, Moscow, Russia
2 Researches and Design Institute of Information Technology, Automation and Telecommunications on Railway Transport (JSC “NIIAS”), Moscow, Russia
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For a description of the information space it is introduced a vector representation of the constituent text documents that are bound by the events described in the timeline. The predicted event is also represented by a vector obtained on the base of its text description. The mean value of projections of the information space in the direction of the vector of predicted events at different time points is considered as a set of information system states. It is also entered the change values of states. To describe transitions between states is used a probabilistic approach and the difference transition scheme. This makes it possible to get the dependence of the time for the value of the probability density for the event “detection information system in a state” in the form of a second order differential equation. On the basis of this equation is formulated and solved the boundary problem. Carried out by the authors the analysis of the stochastic dynamics of achievement a threshold of realization of news events has allowed the establishing of the ability to increase the probability of transition almost simultaneously with the beginning of the process of the news cluster structure changing. This is due to the presence of the memory of previous states in the information system and the possibility of self-description, as a result of accounting in the differential model information processes on the basis of the second derivative over time. In addition, the proposed model demonstrates the possibility of sudden changes in the probability of crossing the threshold of events and takes into account the presence of oscillations in her behavior. Based on the model developed it is proposed the algorithm for analysis of news clusters relationship in the information field with the possibility of occurrence of the predicted event, and determined the possible time of its implementation.
© The Authors, published by EDP Sciences, 2017
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