Rumor Forwarding Prediction Model on Weibo Based on Uncertain Time Series

Abstract

The rapid spread of rumors on social media is mainly caused by individual retweets. This paper applies uncertain time series analysis to analyze a rumor retweeting behavior on Weibo. First, the rumor forwarding is modeled using uncertain time series, including order selection, parameter estimation, residual analysis, uncertain hypothesis test and forecast and the validity of using uncertain time series analysis is further supported by analyzing the characteristics of the residual plot. The experimental results show that the uncertain time series analysis can better predict the next stage of rumor forwarding. The results of the study have important practical significance for rumor management and the management of social media information dissemination.