sis 18: e10

Research Article

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

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  • @ARTICLE{10.4108/eai.13-7-2018.163973,
        author={Akshay Aggarwal and Aniruddha Chauhan and Deepika Kumar and Mamta Mittal and Sharad Verma},
        title={Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={4},
        keywords={Fake news, Transfer learning, Deep learning, Natural language processing},
        doi={10.4108/eai.13-7-2018.163973}
    }
    
  • Akshay Aggarwal
    Aniruddha Chauhan
    Deepika Kumar
    Mamta Mittal
    Sharad Verma
    Year: 2020
    Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.163973
Akshay Aggarwal1, Aniruddha Chauhan1, Deepika Kumar1,*, Mamta Mittal2, Sharad Verma1
  • 1: Department of Computer Science & Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi-110063
  • 2: Department of Computer Science & Engineering, G. B. Government Engineering College, New Delhi-110020
*Contact email: deepika.kumar@bharatividyapeeth.edu

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.