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ISSN 2063-5346
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FAKE NEWS DETECTION USING HYBRID APPROACH

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NAVYA ARYA, PROMILA BAHADUR, TULIKA NARANG, DIVAKAR YADAV
» doi: 10.48047/ecb/2023.12.Si6.035

Abstract

In According to the definition of fake news, it is "misleading information from news sources that pretend to be real but are fake." The dissemination of false information seeks to spread rumours, damage someone's reputation, or generate revenue through clickbait, among other things. Everyone has access to the internet and has a social media profile, so bogus news might spread quickly. Fake news is on the rise, which creates a serious issue that needs to be addressed. In our work, we evaluate and examine each of the mentioned Deep Learning and Machine Learning models: CNN, RNN, LSTM, Bi-LSTM, Random Forest Multinomial Naive Bayes, Decision Tree, and so on. Eighty-eight to ninety-nine percent accuracy is what these algorithms can achieve. Our hybrid FND (Fake News Detection) model, includes layers of Convolution Neural Networks, Bi-Long Short Term Memory, and Bi-Recurrent Neural Networks. The ISOT dataset is used to train the FND model. There are twelve thousand six hundred false and real news articles each in the dataset. Comparing the FND model to the other studied techniques, it has demonstrated superior accuracy. We discovered optimistic outcomes in terms of success rate using this method.

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