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A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media

Author

Listed:
  • Wingyan Chung

    (The University of Texas at Tyler)

  • Yinqiang Zhang

    (The University of Hong Kong)

  • Jia Pan

    (The University of Hong Kong)

Abstract

The spreading of disinformation in social media threatens cybersecurity and undermines market efficiency. Detecting disinformation is challenging due to large volumes of social media content and a rapidly changing environment. This research developed and validated a theory-based, novel deep-learning approach (called TRNN) to disinformation detection. Grounded in social and psychological theories, TRNN uses deep-learning and data-centric augmentation to enhance disinformation detection in financial social media. Temporal and contextual information is encoded as specific knowledge about human-validated disinformation, which was identified from our unique collection of 745,139 financial social media messages about four U.S. high-tech company stocks and their fine-grained trading data. TRNN uses multiple series of long short-term memory (LSTM) recurrent neurons to learn dynamic and hidden patterns to support disinformation detection. Our experimental findings show that TRNN significantly outperformed widely-used machine learning techniques in terms of precision, recall, F-score and accuracy, achieving consistently better classification performance in disinformation detection. A case study of Apple Inc.’s stock price movement demonstrates the potential usability of TRNN for secure knowledge management. The research contributes to developing novel approach and model, producing new information systems artifacts and dataset, and providing empirical findings of detecting online disinformation.

Suggested Citation

  • Wingyan Chung & Yinqiang Zhang & Jia Pan, 2023. "A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media," Information Systems Frontiers, Springer, vol. 25(2), pages 473-492, April.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:2:d:10.1007_s10796-022-10327-9
    DOI: 10.1007/s10796-022-10327-9
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    References listed on IDEAS

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    1. Canh V. Pham & Quat V. Phu & Huan X. Hoang & Jun Pei & My T. Thai, 2019. "Minimum budget for misinformation blocking in online social networks," Journal of Combinatorial Optimization, Springer, vol. 38(4), pages 1101-1127, November.
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    6. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
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    9. Sheikh Rabiul Islam & Sheikh Khaled Ghafoor & William Eberle, 2018. "Mining Illegal Insider Trading of Stocks: A Proactive Approach," Papers 1807.00939, arXiv.org, revised Nov 2018.
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    Cited by:

    1. Sagar Samtani & Ziming Zhao & Ram Krishnan, 2023. "Secure Knowledge Management and Cybersecurity in the Era of Artificial Intelligence," Information Systems Frontiers, Springer, vol. 25(2), pages 425-429, April.

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