Early detection of microblog rumors based on ensemble learning

The early detection of microblog rumors plays an important role in the prevention and control of rumors. However, it is difficult to detect rumors due to the lack of relevant information in the early stage of rumor occurrence. In this paper, the early detection of microblog rumors is realized by selecting effective detection characteristics and combining multiple detection algorithms. In the term of selecting detection characteristics, this paper constructs the emotional characteristics of users and microblog through the emotional analysis of microblog text and user's historical text instead of using the information extracted from comments and forwards. In the term of detection algorithm, the ensemble learning method is adopted as the rumor detection algorithm. The base model is composed of multiple heterogeneous deep learning models. The random forest algorithm is used in the meta model to combine different models in the way of secondary training on the prediction output of the base model to improve the detection accuracy. Experiments show that this method has a good detection effect in the early detection of rumors.