A Survey on Natural Language Processing for Fake News Detection
arXiv:1811.00770
Abstract
Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spread of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news detection is a practical NLP problem useful to all online content providers, in order to reduce the human time and effort to detect and prevent the spread of fake news. In this paper, we describe the challenges involved in fake news detection and also describe related tasks. We systematically review and compare the task formulations, datasets and NLP solutions that have been developed for this task, and also discuss the potentials and limitations of them. Based on our insights, we outline promising research directions, including more fine-grained, detailed, fair, and practical detection models. We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.
11 pages, no figure, Accepted to LREC 2020
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Cited by in corpus (18)
- A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
- A Benchmark Study of Machine Learning Models for Online Fake News Detection
- Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language
- The State of Human-centered NLP Technology for Fact-checking
- Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic
- Deep Two-path Semi-supervised Learning for Fake News Detection
- Network-based Fake News Detection: A Pattern-driven Approach
- Fake News Early Detection: An Interdisciplinary Study
- NewsEmbed: Modeling News through Pre-trained Document Representations
- Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection
- Text Analysis in Adversarial Settings: Does Deception Leave a Stylistic Trace?
- Two-path Deep Semi-supervised Learning for Timely Fake News Detection
- SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?
- A Systematic Review on the Detection of Fake News Articles
- Connecting the Dots Between Fact Verification and Fake News Detection
- Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
- VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text
- A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks