@inproceedings{a997b3a6a3064d5dbd0301a133be2568,
title = "All the Wiser: Fake News Intervention Using User Reading Preferences",
abstract = "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.",
author = "Lo, {Kuan Chieh} and Dai, {Shih Chieh} and Aiping Xiong and Jing Jiang and Ku, {Lun Wei}",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 14th ACM International Conference on Web Search and Data Mining, WSDM 2021 ; Conference date: 08-03-2021 Through 12-03-2021",
year = "2021",
month = aug,
day = "3",
doi = "10.1145/3437963.3441696",
language = "English (US)",
series = "WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining",
publisher = "Association for Computing Machinery, Inc",
pages = "1069--1072",
booktitle = "WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining",
}