Exploiting ensembled neural network model for social platform rumor detection

Abstract

With the spread of the internet and social media, it has become difficult to detect rumors from the vast amount of event information. In order to improve the accuracy of rumor detection, deep learning neural network models are often used in rumor detection tasks. First, this paper reproduces the rumor detection experiments of four single neural network models: Long Short-term Memory Networks (LSTM), Text Convolutional Neural Networks (TextCNN), Text Recurrent Neural Network with Attention Mechanism (TextRNN_Att), and Transformer. On this basis, a model based on pre-trained feature extractor and ensemble learning is proposed, and a weighted average ensemble algorithm is adopted. The results show that the rumor-detecting ensemble learning model is better than the single model in all indicators. Then, aiming at the problem that the weighted average ensemble method cannot determine the optimal ensemble parameters, this paper proposes to improve the adaptive ensemble model. Multilayer Perceptron (MLP) is selected as the metamodel, and the weight parameters are automatically trained finetuning on the predicted output of the base model by weighted summation and MLP neural network is used, which improves the traditional integrated weighted average model and realizes the function of automatic weight adjustment. Finally, the Fast Gradient Sign Method (FGSM) algorithm is used to train the model adversarily. The results show that the ensemble model after adversarial training obtains stronger generalization, robustness and attack resistance under the premise of ensuring that the classification performance is not reduced.