The proliferation of fake news has grown into a global concern with adverse socio-political and economical impact. In recent years, machine learning has emerged as a promising approach to the automation of detecting and tracking fake news at scale. Current state of the art in the identification of fake news is generally focused on semantic analysis of the text, resulting in 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 contextually 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 network in addition to the features extracted in the previous steps. Experimental evaluation of our approach shows classification accuracy of 97% on the Politifact dataset. We also examined the generalizability of our proposed model on the BuzzFeed dataset, resulting in an accuracy 89.50%.
The rampant integration of social media in our every day lives and culture has given rise to fast and easier access to the flow of information than ever in human history. However, the inherently unsupervised nature of social media platforms has also made it easier to spread false information and fake news. Furthermore, the high volume and velocity of information flow in such platforms make manual supervision and control of information propagation infeasible. This paper aims to address this issue by proposing a novel deep learning approach for automated detection of false short-text claims on social media. We first introduce Sentimental LIAR, which extends the LIAR dataset of short claims by adding features based on sentiment and emotion analysis of claims. Furthermore, we propose a novel deep learning architecture based on the DistilBERT language model for classification of claims as genuine or fake. Our results demonstrate that the proposed architecture trained on Sentimental LIAR can achieve an accuracy of 70%, which is an improvement of 30% over previously reported results for the LIAR benchmark.
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