Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2021

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.

Keywords

fake news intervention, human-subject experiment, web application

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, March 8-12, Israel and Virtual

First Page

1069

Last Page

1072

ISBN

9781450382977

Identifier

10.1145/3437963.3441696

Publisher

ACM

City or Country

New York

Embargo Period

4-15-2021

Copyright Owner and License

LARC and Authors

Additional URL

https://doi.org/10.1145/3437963.3441696

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