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The proliferation of fake news has grown into a global concern with adverse socio-political and economic impacts. In recent years, machine learning has emerged as a promising approach to the automation of detecting and tracking fake news at scale. The current state of the art in the identification of fake news is generally focused on semantic analysis of the text, resulting in a promising performance in automated detection of fake news. However, fake news campaigns are also evolving in response to such new technologies by mimicking semantic features of genuine news, which can significantly affect the performance of fake news classifiers trained on con-textually limited features. In this work, we propose a novel hybrid deep learning model for fake news detection that augments the semantic characteristics of the news with features extracted from the structure of the dissemination network. To this end, we first extend the LIAR dataset by integrating sentiment and affective features to the data and then use a BERT-based model to obtain a representation of the text. Moreover, we propose a novel approach for fake news detection based on Graph Attention Networks to leverage the user-centric features and graph features of news residing social networks in addition to the features extracted in the previous steps. We examined the generalizability of our proposed model on the BuzzFeed dataset, resulting in an accuracy of 89.50%.
Current Team Members:
Bibek Upadhayay
PI: Vahid Behzadan
Affiliate Organizations:
Tools and Datasets:
Code: Github
Dataset: Github (Forked from: Github )
Publications: