Abstract
Fake news is deliberately created with the goal of influencing people and their belief systems. Because false news has a detrimental influence on society and politics, it has become increasingly crucial to identify and stop it from spreading. Most of the prior research has employed supervised learning but has placed emphasis on the terms that were used in the dataset. Initially, we began by pre-processing the information (replacing the missing value, noise removal, tokenization, and stemming). LSTM (long short-term memory network) model is employed for text data classification in this article, and we deal with automated feature selection from text data using the GloVe model. Unlike previous models, the suggested model can select the relevant attributes to determine whether the news is false or real while existing models fail in this regard. The proposed model outperforms the already available models.
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