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Explainable Deep Learning for False Information Identification: An Argumentation Theory Approach

Author

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  • Kyuhan Lee

    (Management Information Systems, Korea University Business School, Seoul 02841, South Korea)

  • Sudha Ram

    (Management Information Systems, University of Arizona, Tucson, Arizona 85721)

Abstract

In today’s world, where online information is proliferating in an unprecedented way, a significant challenge is whether to believe the information we encounter. Ironically, this flood of information provides us with an opportunity to combat false claims by understanding their nature. That is, with the help of machine learning, it is now possible to effectively capture the characteristics of false information by analyzing massive amounts of false claims published online. These methods, however, have neglected the nature of human argumentation, delegating the process of making inferences of the truth to the black box of neural networks. This has created several challenges (namely latent text representations containing entangled syntactic and semantic information, an irrelevant part of text being considered when abstracting text as a latent vector, and counterintuitive model explanation). To resolve these issues, based on Toulmin’s model of argumentation, we propose a computational framework that helps machine learning for false information identification (FII) understand the connection between a claim (whose veracity needs to be verified) and evidence (which contains information to support or refute the claim). Specifically, we first build a word network of a claim and evidence reflecting their syntaxes and convert it into a signed word network using their semantics. The structural balance of this word network is then calculated as a proxy metric to determine the consistency between a claim and evidence. The consistency level is fed into machine learning as input, providing information for verifying claim veracity and explaining the model’s decision making. The two experiments for testing model performance and explainability reveal that our framework shows stronger performance and better explainability, outperforming cutting-edge methods and presenting positive effects on human task performance, trust in algorithms, and confidence in decision making. Our results shed new light on the growing field of automated FII.

Suggested Citation

  • Kyuhan Lee & Sudha Ram, 2024. "Explainable Deep Learning for False Information Identification: An Argumentation Theory Approach," Information Systems Research, INFORMS, vol. 35(2), pages 890-907, June.
  • Handle: RePEc:inm:orisre:v:35:y:2024:i:2:p:890-907
    DOI: 10.1287/isre.2020.0097
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    References listed on IDEAS

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    1. Dongmin Kim & Izak Benbasat, 2006. "The Effects of Trust-Assuring Arguments on Consumer Trust in Internet Stores: Application of Toulmin's Model of Argumentation," Information Systems Research, INFORMS, vol. 17(3), pages 286-300, September.
    2. Keith Jacks Gamble & Patricia A. Boyle & Lei Yu & David A. Bennett, 2015. "How Does Aging Affect Financial Decision Making?," Issues in Brief ib2015-1, Center for Retirement Research.
    3. Keith Jacks Gamble & Patricia A. Boyle & Lei Yu & David A. Bennett, 2015. "Aging and Financial Decision Making," Management Science, INFORMS, vol. 61(11), pages 2603-2610, November.
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    Cited by:

    1. Ahmed Abbasi & Jeffrey Parsons & Gautam Pant & Olivia R. Liu Sheng & Suprateek Sarker, 2024. "Pathways for Design Research on Artificial Intelligence," Information Systems Research, INFORMS, vol. 35(2), pages 441-459, June.

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