Enhancing Early Detection of Fake News on Social Media with a Dual-Branch Neural Network Model
Keywords:
Fake News Detection, Social Media, Dual-Branch Network, Early Detection, Text Features, Semantic Relevance, Convolutional Neural Network, Generalized Mean Pooling.Abstract
The widespread dissemination of fake news on social media has brought about the various degrees of negative impact on society. To address the issue of insufficient social context data in the early identification of erroneous information, we suggest a model that combines dual-branch network training. A max-pooling branch (MPB) and a generalised mean pooling branch (GPB) are the two main parts of this approach. While the GPB adds trainable pooling layers to capture the underlying semantic qualities of news articles, the MPB uses a convolutional neural network to extract text attributes from the articles. Additionally, every branch network evaluates the semantic importance of the news headline about the body text. Ultimately, judgements about the veracity of the news are based on the combined output of these two branch networks’ cooperative training. The experimental results show that the suggested model outperforms baseline models in evaluation metrics such as accuracy, recall, and F1- Score, with an astounding F1- score of 94.1%.
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