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• W4289778915 startingPage "103674" @default.
• W4289778915 abstract "People are easily duped by fake news and start to share it on their networks. With high frequency, fake news causes panic and forces people to engage in unethical behavior such as strikes, roadblocks, and similar actions. Thus, counterfeit news detection is highly needed to secure people from misinformation on social platforms. Filtering fake news manually from social media platforms is nearly impossible, as such an act raises security and privacy concerns for users. As a result, it is critical to assess the quality of news early on and prevent it from spreading. In this article, we propose an automated model to identify fake news at an early stage. Machine learning-based models such as Random Forest, Logistic Regression, Naïve Bayes, and K-Nearest Neighbor are used as baseline models, implemented with the features extracted using countvectorizer and tf–idf. The baseline and other existing model outcomes are compared with the proposed deep learning-based Long–Short Term Memory (LSTM) network. Experimental results show that different settings achieved an accuracy of 99.82% and outperformed the baseline and existing models." @default.
• W4289778915 created "2022-08-04" @default.
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• W4289778915 date "2023-03-01" @default.
• W4289778915 modified "2023-10-16" @default.
• W4289778915 title "Securing social platform from misinformation using deep learning" @default.
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• W4289778915 doi "https://doi.org/10.1016/j.csi.2022.103674" @default.
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