Topic Trend Prediction Based on Wavelet Transformation

Abstract

The research of topic trend prediction can be a good reference for maximizing the propagation effects of network advertisements as well as guiding and controlling the network consensus. This paper proposes PTEP (the Prediction of Topic Energy Peak) method to model the life cycle of a topic and predicts the time when a hot topic will outbreak. Firstly, taking the number and the authority of followers and the interest of users to a topic into consideration, we design a topic-related user authority (TRUA) algorithm to measure the authority of users. Secondly, we calculate the energy value considering both the tweets and users authority related to the topic. Thirdly, we measure the fluctuation of the energy value based on wavelet transformation. Finally, we present rules to predict topic trend. Experimental results show that our method can effectively predict the peak of a topic in advance with a low omission rate.