All the wiser: Fake news intervention using user reading preferences

• Main Authors: LO, Kuan Chieh, DAI, Shih Chieh, XIONG, Aiping, JIANG, Jing, KU, Lun Wei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: fake news intervention; human-subject experiment; web application; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/5893; https://ink.library.smu.edu.sg/context/sis_research/article/6896/viewcontent/3._All_the_Wiser_Fake_News_Intervention_Using_User_Reading_Preferences__Demo___WSDM2021_.pdf
• Institution: Singapore Management University

To address the increasingly significant issue of fake news, we develop a news reading platform in which we propose an implicit approach to reduce people's belief in fake news. Specifically, we leverage reinforcement learning to learn an intervention module on top of a recommender system (RS) such that the module is activated to replace RS to recommend news toward the verification once users touch the fake news. To examine the effect of the proposed method, we conduct a comprehensive evaluation with 89 human subjects and check the effective rate of change in belief but without their other limitations. Moreover, 84% participants indicate the proposed platform can help them defeat fake news. The demo video is available on YouTube https://youtu.be/wKI6nuXu-SM.