Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model

Authors

  • Akshay Aggarwal Bharati Vidyapeeth’s College of Engineering
  • Aniruddha Chauhan Bharati Vidyapeeth’s College of Engineering
  • Deepika Kumar Bharati Vidyapeeth’s College of Engineering
  • Sharad Verma Bharati Vidyapeeth’s College of Engineering
  • Mamta Mittal G. B. Government Engineering College

DOI:

https://doi.org/10.4108/eai.13-7-2018.163973

Keywords:

Fake news, Transfer learning, Deep learning, Natural language processing

Abstract

With the ever-increasing rate of information dissemination and absorption, “Fake News” has become a real menace. People these days often fall prey to fake news that is in line with their perception. Checking the authenticity of news articles manually is a time-consuming and laborious task, thus, giving rise to the requirement for automated computational tools that can provide insights about degree of fake ness for news articles. In this paper, a Natural Language Processing (NLP) based mechanism is proposed to combat this challenge of classifying news articles as either fake or real. Transfer learning on the Bidirectional Encoder Representations from Transformers (BERT) language model has been applied for this task. This paper demonstrates how even with minimal text pre-processing, the fine-tuned BERT model is robust enough to perform significantly well on the downstream task of classification of news articles. In addition, LSTM and Gradient Boosted Tree models have been built to perform the task and comparative results are provided for all three models. Fine-tuned BERT model could achieve an accuracy of 97.021% on NewsFN data and is able to outperform the other two models by approximately eight percent.

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Published

09-04-2020

How to Cite

1.
Aggarwal A, Chauhan A, Kumar D, Verma S, Mittal M. Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model. EAI Endorsed Scal Inf Syst [Internet]. 2020 Apr. 9 [cited 2024 Nov. 25];7(27):e10. Available from: https://publications.eai.eu/index.php/sis/article/view/2113