Machine Learning Technique Based Fake News Detection
Biplob Kumar Sutradhar Md. Zonaid Nushrat Jahan Ria Sheak Rashed Haider Noori
False news has received attention from both the general public and the
scholarly world. Such false information has the ability to affect public
perception, giving nefarious groups the chance to influence the results of
public events like elections. Anyone can share fake news or facts about anyone
or anything for their personal gain or to cause someone trouble. Also,
information varies depending on the part of the world it is shared on. Thus, in
this paper, we have trained a model to classify fake and true news by utilizing
the 1876 news data from our collected dataset. We have preprocessed the data to
get clean and filtered texts by following the Natural Language Processing
approaches. Our research conducts 3 popular Machine Learning (Stochastic
gradient descent, Na\"ive Bayes, Logistic Regression,) and 2 Deep Learning
(Long-Short Term Memory, ASGD Weight-Dropped LSTM, or AWD-LSTM) algorithms.
After we have found our best Naive Bayes classifier with 56% accuracy and an
F1-macro score of an average of 32%.