Time-Dependent Prediction of Microblog Propagation Trends Based on Group Features

Abstract

The conventional machine learning-based method for the prediction of microblogs’ reposting number mainly focuses on the extraction and representation of static features of the source microblogs such as user attributes and content attributes, without taking into account the problem that the microblog propagation network is dynamic. Moreover, it neglects dynamic features such as the change of the spatial and temporal background in the process of microblog propagation, leading to the inaccurate description of microblog features, which reduces the performance of prediction. In this paper, we contribute to the study on microblog propagation trends, and propose a new microblog feature presentation and time-dependent prediction method based on group features, using a reposting number which reflects the scale of microblog reposting to quantitatively describe the spreading effect and trends of the microblog. We extract some dynamic features created in the process of microblog propagation and development, and incorporate them with some traditional static features as group features to make a more accurate presentation of microblog features than a traditional machine learning-based research. Subsequently, based on the group features, we construct a time-dependent model with the LSTM network for further learning its hidden features and temporal features, and eventually carry out the prediction of microblog propagation trends. Experimental results show that our approach has better performance than the state-of-the-art methods.