Fake news has become a major issue, causing harm to both individuals and society as a whole. With so much information circulating online, it is impossible to manually verify every piece of news. This is especially true because fake news can be cleverly disguised, making it hard to spot. Advanced language models are often used to create convincing but false reviews, which can mislead people when they are shopping online. This research looks into how blockchain technology and sentiment analysis can be used together to create a system that protects privacy while detecting and analyzing fake web recommendations.

The system uses sentiment-based features taken from web recommendations as its input data. A special type of neural network, called a generative convolutional Bernoulli Bayes neural network, is used to extract and classify these features. To make the network even more secure, blockchain technology is combined with federated learning. This means that the data stays private and is not shared with a central server.

The research tests the system using different datasets of fake recommendations. The performance is measured using several metrics, including accuracy, precision, recall, and F-measure. Each classifier is thoroughly evaluated to see how well it works. The results show that a predictive model can be created using tweet data. This model can tell the difference between spam and non-spam content and can also determine the sentiment behind it. The proposed method achieved impressive results, with 99 percent accuracy, 94 percent precision, 93 percent area under the curve, 94 percent recall, and 96 percent F-measure.

It is important to note that while this research shows promising results, it is just one step in the fight against fake news. As technology advances, so do the methods used to create and spread false information. Therefore, continuous innovation and improvement in detection methods are necessary. Additionally, educating the public about the dangers of fake news and how to spot it is crucial. People need to be critical thinkers and verify information from multiple sources before believing it.

The use of blockchain technology in this system is particularly interesting. Blockchain is known for its security and transparency, making it a great fit for protecting privacy in data analysis. However, it is not without its challenges. The technology is still relatively new and can be complex to implement. Moreover, it requires a lot of computational power, which can be a barrier for some. Despite these challenges, the potential benefits of using blockchain in fake news detection are significant.

In conclusion, the combination of blockchain technology and sentiment analysis offers a powerful tool in the fight against fake news. While the results of this research are promising, it is just the beginning. Continuous efforts are needed to stay ahead of those who create and spread false information. By doing so, we can protect individuals and society from the harmful effects of fake news.

Fighting Fake News: The Power of Blockchain and Sentiment Analysis

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