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TB-BCG: Topic-Based BART Counterfeit Generator for Fake News Detection

Author

Listed:
  • Andrea Stevens Karnyoto

    (State Key Laboratory of Communication Content Cognition, People’s Daily Online, Beijing 100733, China
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Chengjie Sun

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Bingquan Liu

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Xiaolong Wang

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

Abstract

Fake news has been spreading intentionally and misleading society to believe unconfirmed information; this phenomenon makes it challenging to identify fake news based on shared content. Fake news circulation is not only a current issue, but it has been disseminated for centuries. Dealing with fake news is a challenging task because it spreads massively. Therefore, automatic fake news detection is urgently needed. We introduced TB-BCG, Topic-Based BART Counterfeit Generator, to increase detection accuracy using deep learning. This approach plays an essential role in selecting impacted data rows and adding more training data. Our research implemented Latent Dirichlet Allocation (Topic-based), Bidirectional and Auto-Regressive Transformers (BART), and Cosine Document Similarity as the main tools involved in Constraint @ AAAI2021-COVID19 Fake News Detection dataset shared task. This paper sets forth this simple yet powerful idea by selecting a dataset based on topic and sorting based on distinctive data, generating counterfeit training data using BART, and comparing counterfeit-generated text toward source text using cosine similarity. If the comparison value between counterfeit-generated text and source text is more than 95%, then add that counterfeit-generated text into the dataset. In order to prove the resistance of precision and the robustness in various numbers of data training, we used 30%, 50%, 80%, and 100% from the total dataset and trained it using simple Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Compared to baseline, our method improved the testing performance for both LSTM and CNN, and yields are only slightly different.

Suggested Citation

  • Andrea Stevens Karnyoto & Chengjie Sun & Bingquan Liu & Xiaolong Wang, 2022. "TB-BCG: Topic-Based BART Counterfeit Generator for Fake News Detection," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:585-:d:749154
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    References listed on IDEAS

    as
    1. Ozbay, Feyza Altunbey & Alatas, Bilal, 2020. "Fake news detection within online social media using supervised artificial intelligence algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    2. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
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