TY - GEN
T1 - An explainable machine learning framework for fake financial news detection
AU - Zhang, Xiaohui
AU - Du, Qianzhou
AU - Zhang, Zhongju
N1 - Publisher Copyright:
© ICIS 2020. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Explainable machine learning
KW - Fake financial news
KW - Social media platform
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M3 - Conference contribution
AN - SCOPUS:85103442321
T3 - International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global
BT - International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive
PB - Association for Information Systems
T2 - 2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020
Y2 - 13 December 2020 through 16 December 2020
ER -