Fake News Detection
151 papers with code • 9 benchmarks • 25 datasets
Fake News Detection is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake. The goal of fake news detection is to develop algorithms that can automatically identify and flag fake news articles, which can be used to combat misinformation and promote the dissemination of accurate information.
Benchmarks
These leaderboards are used to track progress in Fake News Detection
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Libraries
Use these libraries to find Fake News Detection models and implementationsDatasets
LIAR
RealNews
Weibo NER
FakeNewsNet
Snopes
FNC-1
Latest papers with no code
MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions
MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models.
MSynFD: Multi-hop Syntax aware Fake News Detection
These existing methods fail to handle the complex, subtle twists in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing.
User Modeling and User Profiling: A Comprehensive Survey
This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.
LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection
However, a significant challenge in identifying fake news is the limited availability of labeled news datasets.
Prompting with Divide-and-Conquer Program Makes Large Language Models Discerning to Hallucination and Deception
Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications.
Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection
In this paper, we focus on neural fake news, which refers to content generated by neural networks aiming to mimic the style of real news to deceive people.
Fact-checking based fake news detection: a review
This paper reviews and summarizes the research results on fact-based fake news from the perspectives of tasks and problems, algorithm strategies, and datasets.
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It
Fake news detection models are critical to countering disinformation but can be manipulated through adversarial attacks.
A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT
The proliferation of fake news has emerged as a critical issue in recent years, requiring significant efforts to detect it.
GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking
With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability.
