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ISSN 2063-5346
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FAKE NEWS DETECTION IN CHATTING APPLICATION WITH RANDOM FOREST OVER LSTM

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Kasturi pogiri, S.John Justin Thangaraj
» doi: 10.31838/ecb/2023.12.sa1.480

Abstract

Aim: The aim of the proposed research is to develop false news detection using the Random forest model and improve accuracy with neural networks in contrast to the long short term memory model. Materials and Methods: The random forest model is applied on data, which is a text file containing sequences, a collection of words LSTM for predicting the accuracy of fake news that compares two sources. Model of long-term short-term memory. It has been suggested and developed to have random forests. The size of the sample The G Power value of 0.8 was used to calculate the number of people in each category. The precision was excellent. Random forest (56 percent) was the most effective in spotting bogus news. When compared to LSTM, the least mean error is (40%). Results: The accuracy was maximum in detecting the fake news in social media using random forest 56% with long short term memory model 40% for the same dataset. Conclusion: The study proves that random forest exhibits better accuracy than long short term memory in detecting the fake news on e-news applications.

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