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DETECTING FAKE NEWS WITH LSTM: A DEEP LEARNING APPROACH
Fake news has become a significant concern in today's digital age, as it can easily mislead and deceive people. One of the fundamental steps to combat fake news is through fake news detection. In recent years, research has focused on developing effective techniques for detecting fake news using Natural Language Processing. It further affects their emotions and cultural values, creating a tense among people, and instigating anger against fellow people or the government. This further changes the order in society causing hate speeches, riots, strikes, or even fatal accidents that affect innocent lives. When situations like these affect society, it’s wise that we utilize the necessary technologies in aid to eliminate this kind of issue. One such tech that’s disrupting human language processing is Natural Language Processing (NLP) which processes information like text, audio, etc. between languages and text, and audio formats. Leveraging this technology to process the text and analyze the motive and sentiment could shed light on the reliability of the trueness of the text. As a result of this, we could develop a fake news detection system using NLP. To develop such a system, we have to train the machine learning model with an existing dataset. Based on the knowledge gained from this training, the model would be able to identify a pattern in the test data we give. Here, training the model plays an important role as it affects the accuracy of the model in giving the final output. This model can be evaluated by testing it with real and fake news articles. We can develop a better understanding of how to detect fake news from the result generated by the model and we can fine-tune it in order to attain a higher accuracy level. These developed accuracy models can be used by others for various purposes in NLP to build better adapting models that make the life of humans easier.
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