XFlag: Explainable Fake News Detection Model on Social Media

Abstract

Social media allows any individual to disseminate information without third-party restrictions, making it difficult to verify the authenticity of a source. The proliferation of fake news has severely affected people’s intentions and behaviors in trusting online sources. Applying AI approaches for fake news detection on social media is the focus of recent research, most of which, however, focuses on enhancing AI performance. This study proposes XFlag, an innovative explainable AI (XAI) framework which uses long short-term memory (LSTM) model to identify fake news articles, layer-wise relevance propagation (LRP) algorithm to explain the fake news detection model based on LSTM, and situation awareness-based agent transparency (SAT) model to increase transparency in human-AI interaction. The developed XFlag framework has been empirically validated. The findings suggest the use of XFlag supports users in understanding system goals (perception), justifying system decisions (comprehension), and predicting system uncertainty (projection), with little cost of perceived cognitive workload.

Notes

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Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan, under Grant MOST 109-2410-H-004-067-MY2 and MOST 109-2221-E-004-008-MY3.

Notes on contributors

Shih-Yi Chien

Shih-Yi Chien is an assistant professor in the Department of Management Information Systems at National Chengchi University, Taiwan. His research interests include human-robot interaction, trust in automation, and XAI. His research has appeared in Human Factors, IEEE Transactions on Human-Machine Systems, ACM Transactions on Interactive Intelligent Systems, and others.

Cheng-Jun Yang

Cheng-Jun Yang is a graduate student in the Department of Management Information Systems at National Chengchi University, Taiwan. His current research interests include machine learning, XAI, human-robot interaction, and human-automation collaboration.

Fang Yu

Fang Yu is an associate professor in the Department of Management Information Systems at National Chengchi University. He received his Ph.D. degree and the 2010 Outstanding Dissertation Award in Computer Science from the University of California, Santa Barbara. His research interests span software verification, security, string analysis, and distributed computation.