Authentication of Broadcast News on Social Media Using Machine Learning

This chapter discusses the training model that identifies the fake news, based on a certain degree of accuracy. The literature survey reveals that machine learning techniques provide significant support for detecting fake news. The prime motive of feature extraction is to reduce the processing time of the compiler. Therefore, it is a challenging task to use machine learning algorithms for detecting fake or wrong news. News is considered to be a fake if it is factually incorrect, misinterprets the facts, or spreads through any unauthenticated media. The prime reason for its popularity and demand is its cost-effectiveness that encourages multiple users to communicate interactively with their peers. Data retrieval is an important process that aims to retrieve data from various sources. Once the data is downloaded from social media, it is pre-processed to remove any punctuation symbols and an emoticons.