Forwarding in Social Media: Forecasting Popularity of Public Opinion With Deep Learning

Abstract

The forwarding behavior of social media users within social circles facilitates intensive discussions of specific social events in cyberspace, significantly contributing to the dissemination and development of public opinions. Existing models for calculating the popularity of public opinion (PPO) overlook the effects of forwarding behavior. This article addresses this gap with two primary objectives: 1) by developing a calculation model for PPO that integrates the forwarding dynamics within social networks; and 2) by establishing a predictive model that is applied to the temporal evolution of forwarding circles, thus enabling a time-series prediction for PPO. The approach commenced by determining the information entropy based on the structural attributes of forwarding circles. Then, we assess the similarity between information entropy production and the Baidu search index to validate the calculation model’s accuracy. Building on this foundation, public opinion data centered around 30 social events with a total sample size of 15.567 million blogs were collected for modeling. Finally, we design a deep learning algorithm to predict the PPO trend. The results demonstrate that the information entropy of forwarding circles accurately represents PPO, and the proposed predictive model can capture the time-series evolution trend of PPO on social media. These findings offer valuable insights into public opinion analysis and present a robust method for academics and social media practitioners.