Abstract
In the recent years, social media acts as double-edged sword for the society as it is being used for exchanging real as well as fake news. Large number of researchers are involved for the detection of fake news using user credibility, content and propagation-based features. This paper proposes PropFND (Propagation based Fake News Detection) model to classify news as real or fake based on the combination of propagation pattern and user profile features. For the training of proposed model, we used combined features and applied several classifiers and finally concluded that Support Vector Machine (SVM) gives an improved result. This proposed model gives the improved accuracy of 93.81% which is higher than state-of-the-art model. After experimental analysis we noticed that real news propagates for long duration as compared to the fake news.
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Verma, P.K., Agrawal, P. (2022). PropFND: Propagation Based Fake News Detection. In: Unhelker, B., Pandey, H.M., Raj, G. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 925. Springer, Singapore. https://doi.org/10.1007/978-981-19-4831-2_45
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DOI: https://doi.org/10.1007/978-981-19-4831-2_45
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