ABSTRACT
In the current years, with the rapid improvement of online social networks, much fake news for numerous commercial and political purposes has appeared and spread in the online world. Words can easily infect this online fake news, which has already had a substantial effect on of each fake offline society. A key aim in improving the authenticity of information in online social networks is the timely detection of fake news. Fake news is a shape of yellow journalism that summarizes clearly false information and is usually disseminated via social and other online media. This is often done to sell or impose a specific idea and is often executed with a political agenda. Such messages can contain false or exaggerated claims and can end up going viral by algorithms, putting users in a filter bubble. This advanced Python Fake News Detection project deals with fake news and actual news. This can be done by creating a TF-IDF Vectorizer on the dataset using Sklearn. Next, initialize a Passive-Aggressive classifier and in-shape the version. The accuracy score and the confusion matrix indicate the performance of the model.
