Combating Fake News: A Survey on Identification and Mitigation Techniques
January 18, 2019 Β· The Cartographer Β· π ACM Transactions on Intelligent Systems and Technology
"No code URL or promise found in abstract"
"Title-pattern auto-detect: Combating Fake News: A Survey on Identification and Mitigation Techniques"
Evidence collected by the PWNC Scanner
Authors
Karishma Sharma, Feng Qian, He Jiang, Natali Ruchansky, Ming Zhang, Yan Liu
arXiv ID
1901.06437
Category
cs.LG: Machine Learning
Cross-listed
cs.AI,
cs.SI,
stat.ML
Citations
332
Venue
ACM Transactions on Intelligent Systems and Technology
Last Checked
1 day ago
Abstract
The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Machine Learning
ποΈ
ποΈ
Transcended
ποΈ
ποΈ
Transcended
Continuous control with deep reinforcement learning
π
π
Old Age
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
π
π
Old Age
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
π
π
Old Age
SGDR: Stochastic Gradient Descent with Warm Restarts
ποΈ
ποΈ
Transcended