TY - GEN
T1 - Domain Adaptive Fake News Detection via Reinforcement Learning
AU - Mosallanezhad, Ahmadreza
AU - Karami, Mansooreh
AU - Shu, Kai
AU - Mancenido, Michelle V.
AU - Liu, Huan
N1 - Funding Information: This research is supported in part by the National Science Foundation under Grant No. CHE-2105032, IIS-2008228, CNS-1845639, CNS-1831669. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Publisher Copyright: © 2022 ACM.
PY - 2022/4/25
Y1 - 2022/4/25
N2 - With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news. Effective fake news detection is a non-trivial task due to the diverse nature of news domains and expensive annotation costs. In this work, we address the limitations of existing automated fake news detection models by incorporating auxiliary information (e.g., user comments and user-news interactions) into a novel reinforcement learning-based model called REinforced Adaptive Learning Fake News Detection (REAL-FND). REAL-FND exploits cross-domain and within-domain knowledge that makes it robust in a target domain, despite being trained in a different source domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model, especially when limited labeled data is available in the target domain.
AB - With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news. Effective fake news detection is a non-trivial task due to the diverse nature of news domains and expensive annotation costs. In this work, we address the limitations of existing automated fake news detection models by incorporating auxiliary information (e.g., user comments and user-news interactions) into a novel reinforcement learning-based model called REinforced Adaptive Learning Fake News Detection (REAL-FND). REAL-FND exploits cross-domain and within-domain knowledge that makes it robust in a target domain, despite being trained in a different source domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model, especially when limited labeled data is available in the target domain.
KW - disinformation
KW - domain adaptation
KW - neural networks
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85129895573&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129895573&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3485447.3512258
DO - https://doi.org/10.1145/3485447.3512258
M3 - Conference contribution
T3 - WWW 2022 - Proceedings of the ACM Web Conference 2022
SP - 3632
EP - 3640
BT - WWW 2022 - Proceedings of the ACM Web Conference 2022
PB - Association for Computing Machinery, Inc
T2 - 31st ACM World Wide Web Conference, WWW 2022
Y2 - 25 April 2022 through 29 April 2022
ER -