# A Benchmark Study of Machine Learning Models for Online Fake News Detection

## TL;DR

This study benchmarks various machine learning models, especially pre-trained language models like BERT, for online fake news detection across diverse datasets, highlighting their effectiveness and providing insights for future research and practical applications.

## Contribution

It is the first comprehensive benchmark comparing multiple ML approaches, including pre-trained models, on diversified datasets for fake news detection.

## Key findings

- Pre-trained models like BERT outperform traditional methods.
- BERT performs well even with small datasets.
- Model performance varies with article topic and length.

## Abstract

The proliferation of fake news and its propagation on social media has become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been suggested to detect fake news. However, most of those focused on a specific type of news (such as political) which leads us to the question of dataset-bias of the models used. In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one. We explored a number of advanced pre-trained language models for fake news detection along with the traditional and deep learning ones and compared their performances from different aspects for the first time to the best of our knowledge. We find that BERT and similar pre-trained models perform the best for fake news detection, especially with very small dataset. Hence, these models are significantly better option for languages with limited electronic contents, i.e., training data. We also carried out several analysis based on the models' performance, article's topic, article's length, and discussed different lessons learned from them. We believe that this benchmark study will help the research community to explore further and news sites/blogs to select the most appropriate fake news detection method.