Machine Learning Technique Based Fake News Detection

TL;DR

This paper explores machine learning and deep learning methods to detect fake news using a dataset of 1876 news articles, achieving the best Naive Bayes classifier with 56% accuracy.

Contribution

It compares multiple machine learning and deep learning algorithms for fake news detection on a new dataset, highlighting the effectiveness of Naive Bayes.

Findings

Naive Bayes achieved 56% accuracy.

Deep learning models did not outperform traditional ML.

Preprocessing improved data quality for classification.

Abstract

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%.