Diffusion Model for Forecasting Events in News Feeds

Abstract

On the basis of the diffusion theory, we suggested a model for forecasting event in news feeds, which is based on the use of stochastic dynamics of changes in the structure of non-stationary time series in news text clusters (states of the information space). Forecasting events in a news feed is based on their text description, vectorization, and finding the cosine value of the angle between the given vector and the centroids of various information space semantic clusters. Changes over time in the cosine value of such angle between the above vector and centroids can be represented as a point wandering on [0,1] segment. This segment contains a trap at the event occurrence threshold point. The wandering point can fall into this trap over time. We have considered probability patterns of transitions between states in the information space. We have derived a nonlinear second-order differential equation; formulated and solved the boundary value problem of forecasting news events. We have obtained theoretical time dependence for the probability density function of the parameter distribution of non-stationary time series that describe the information space evolution. The results of simulating the time dependence of the event probability (with sets of parameter values of the developed model, which have been experimentally determined for already occurred events) show that the model is consistent and adequate. Experimental verification of the proposed model was carried out using a corpus of texts written in Russian.