Abstract
Spreading fake news has become a serious issue in the current social
media world. It is broadcasted with dishonest intentions to mislead
people. This has caused many unfortunate incidents in different
countries. The most recent one was the latest presidential elections
where the voters were mis lead to support a leader. Twitter is one of
the most popular social media platforms where users look up for real
time news. We extracted real time data on multiple domains through
twitter and performed analysis. The dataset was preprocessed and
user_verified column played a vital role. Multiple machine algorithms
were then performed on the extracted features from preprocessed dataset.
Logistic Regression and Support Vector Machine had promising results
with both above 92% accuracy. Naive Bayes and Long-Short Term memory
didn’t achieve desired accuracies. The model can also be applied to
images and videos for better detection of fake news.