An explainable machine learning framework for fake financial news detection

Xiaohui Zhang, Qianzhou Du, Zhongju Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, we have witnessed a continuing onslaught of fake news, or other forms of biased information on social media platforms. These types of information can influence people's beliefs, attitudes, and behaviors by its ubiquity with significant social and economic implications. In this study, we examine fake news on crowd-sourced platforms for financial markets. Assembling a unique dataset of unambiguous fake news articles that were prosecuted by the Securities and Exchange Commission, along with propagation data of such news on other digital platforms and the financial performance data of the focal firm, we develop a well-justified and explainable machine-learning framework to predict fake financial news on social media platforms. Our framework design is rooted in the Truth Default Theory, which emphasizes contextualized information for deception detection. Extensive analyses are conducted to evaluate the performance and efficacy of the proposed framework.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information Systems, ICIS 2020 - Making Digital Inclusive
Subtitle of host publicationBlending the Local and the Global
PublisherAssociation for Information Systems
ISBN (Electronic)9781733632553
StatePublished - 2020
Event2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020 - Virtual, Online, India
Duration: Dec 13 2020Dec 16 2020

Publication series

NameInternational Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global

Conference

Conference2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020
Country/TerritoryIndia
CityVirtual, Online
Period12/13/2012/16/20

Keywords

  • Explainable machine learning
  • Fake financial news
  • Social media platform

ASJC Scopus subject areas

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences
  • Applied Mathematics

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