Hot topic trend prediction of topic based on markov chain and dynamic backtracking

Abstract

Predicting topic trend in social networks can provide good reference value for public opinion guidance and commercial marketing. In this paper, we discuss the hot topic evaluation methods, and then present a method for evaluating the topic popularity of microblog based on multiple factors, which comprehending four factors (the number of micro blog, number of forwarding, number of comments, and number of praise) and using relative ranking method to define the value of micro blog popularity. In order to improve the prediction accuracy of hot topics, we present a prediction algorithm based on Markov chain and dynamic backtracking, which is based our evaluation method. In the algorithm, we use the simulated annealing method to find the optimal parameters and improve the accuracy of the prediction algorithm based on the Markov chain by historical backtracking. Analysis and simulation results demonstrate that the proposed algorithm is more accurate than some conventional methods.