DETECTING FAKE NEWS THROUGH LOGESTIC REGRESSION

Computer Engineering

When people are exposed to fake news, they might believe false information. This can lead to misguided beliefs and actions based on inaccurate data, affecting their decision-making in areas such as health, politics, and finance. Fake news can shape public opinion and influence elections, policies, and social attitudes. It can sway people’s views on important issues and contribute to polarization in society. Fake news often promotes divisive narratives, fostering hostility and division among different groups of people. This can lead to social unrest and conflicts. False information can influence stock markets, consumer behavior, and investment decisions, potentially causing financial losses for individuals and businesses. In the context of health, fake news can be particularly harmful. We use logestic regression algorithm. Logistic regression is a statistical method used for binary classification tasks, where the goal is to predict one of two possible outcomes, typically labeled as 0 and 1. It’s a type of regression analysis that models the probability of the binary outcome. Logistic regression is widely used in various fields, including healthcare (predicting disease outcomes), marketing (customer churn prediction), and natural language processing (spam detection), among others. It’s a fundamental algorithm in machine learning and serves as a building block for more complex models. This literature review also includes papers discussing algorithms for fake news detection, such as logstic regression, natural language processing and naïve baye Classifier.

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