Abstract
Everyone depends upon various online resources for news in this modern age, where the internet is pervasive. As the use of social media platforms such as Facebook, Twitter, and others has increased, news spreads quickly among millions of users in a short time. The consequences of Fake news are far-reaching, from swaying election outcomes in favor of certain candidates to creating biased opinions. WhatsApp, Instagram, and many other social media platforms are the main source for spreading fake news. This work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data are Random Forest, Logistic Regression, Decision Tree, KNN and Gradient Booster. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.
Highlights
The term ‘Fake news’ refers to the news content that is false, misleading, or fabricated, in which the facts, sources, or quoted statements of the news content are unverified
There has been a rise in the news lately about lynchings and riots that result in mass deaths; fake news detection aims to detect these and stop similar activities, thereby protecting society from these unwelcome violent acts [3]
After TF-IDF vectorization and cleaning the data we train and test them according to these classifiers we got the accuracy for Logistic Regression as 85.04%, Decision Tree as 78.11%, Gradient Boosting as 77.44%, Random Forest as 84.50%, KN Neighbors Classifier as 80.20%
Summary
The term ‘Fake news’ refers to the news content that is false, misleading, or fabricated, in which the facts, sources, or quoted statements of the news content are unverified. Along with the billions of people using social media, there are robots, or bots, residing within. These Bots help to propagate fake news faster and boost up its popularity on social media. Fake news detection is used to avoid rumors from spreading across various platforms, such as social media and messaging platforms. The news given by the user is classified as true or false based on the data collected using Web Scraping. This task uses five various classification models, including Random Forest, Logistic Regression, Decision Tree, KNN, and Gradient Booster. A mixture of these models is tested
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