Treffer: Review on fake news detection system using different optimization techniques.

For a long time, people have debated the issue of fake news. Before the internet, the major source of this information was yellow journalism, which concentrated on crime, gossip, accidents, and humorous news. To avoid unnecessary fatalities, it is critical to detect incorrect information as quickly as possible. People unintentionally propagate misleading information. False information propagators often use physical assault against citizens to convey their point. This loop will be interrupted only when the people and the government work together to detect and combat fake news. Neural networks have been beneficial in recognizing misinformation campaigns propagated over social media. There are many reasons to be concerned about the damage that false news on social media and other media platforms may do to people, communities, and even nations. Finding techniques to detect it is a critical topic of research. This research examines the current literature on false news detection using Python, scikit-learn, and natural language processing (NLP) and picks the most successful conventional machine learning models to develop a product model using supervised machine learning. For feature extraction and tokenization of text data, we propose using Python scikit-learn, which includes utilities like Count Vectorizer and Tiff Vectorizer. Based on the data in the confusion matrix, we will analyze the best-fit features and choose the most accurate ones. [ABSTRACT FROM AUTHOR]