A theory-driven machine learning system for financial disinformation detection

Xiaohui Zhang, Qianzhou Du, Zhongju Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Maliciously false information (disinformation) can influence people's beliefs and behaviors with significant social and economic implications. In this study, we examine news articles on crowd-sourced digital platforms for financial markets. Assembling a unique dataset of financial news articles that were investigated and prosecuted by the Securities and Exchange Commission, along with the propagation data of such articles on digital platforms and the financial performance data of the focal firm, we develop a well-justified machine learning system to detect financial disinformation published on social media platforms. Our system design is rooted in the truth-default theory, which argues that communication context and motive, coherence, information correspondence, propagation, and sender demeanor are major constructs to assess deceptive communication. Extensive analyses are conducted to evaluate the performance and efficacy of the proposed system. We further discuss this study's theoretical implications and its practical value.

Original languageEnglish (US)
Pages (from-to)3160-3179
Number of pages20
JournalProduction and Operations Management
Volume31
Issue number8
DOIs
StatePublished - Aug 2022

Keywords

  • fake news
  • financial disinformation
  • social media platform
  • theory-driven machine learning
  • truth-default theory

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation

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