The role of social media in our day-to-day life has increased rapidlyin recent years. It is now used not only for social interaction, but also as an important platform for exchanging information and news. Twitter, Facebook a micro blogging service, connects millions of users around the world and allows for the real-time propagation of infor- mation and news. Fake news has become a major problem in these social networks. Fake news has become a major problem in these social networks Fake news has vast impact in our modern society. Detecting Fake news is an important step. This work purposes the use of machine learning techniques to detect Fake news, using NLP algorithm. The normalization method is important step for cleans- ing data before using the machine learning method to classify data.This model counts the credibility of content and user reputation. This method developsa method for automating fake news detection on social media by learning to predict accuracy assessments in credibility-focused Twitter dataset.
To study Various Machine Learning algorithm which are applicable to detect and address the fake news detection process and also implement a tool that stimulates efficient and faster decision making using computational algorithm.
To study on how Machine Learning can classify the data collected into two categories.(Real / Fake)
To develop a Natural Processing Language model based on collected data from dataset.
To evaluate the accuracy of developed model
Combat Misinformation: The project addresses the critical issue of fake news by providing users with a tool to identify potentially misleading information. Data Literacy: Users become more aware of the characteristics of fake news, enhancing their data literacy and critical thinking skills. Educational Tool: The project serves as an educational resource to showcase the capabilities of machine learning in real-world applications. Future Enhancements:
Extend the system to handle news articles in different languages for a broader reach. Ensemble Models: Experiment with ensemble methods to combine the predictions of multiple models for improved accuracy.
Incorporate a mechanism to update the model with the latest data and news articles to adapt to evolving patterns of misinformation. The Fake News Detection using Machine Learning project contributes to addressing the growing concern of fake news and misinformation in today's digital world. It empowers users with a tool that leverages machine learning to help them make more informed decisions about the credibility of news sources.