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
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.
DPOD: Domain-Specific Prompt Tuning for Multimodal Fake News Detection
In this work, we explore whether out-of-domain data can help to improve out-of-context misinformation detection (termed here as multi-modal fake news detection) of a desired domain, to address this challenging problem.
ExFake: Towards an Explainable Fake News Detection Based on Content and Social Context Information
ExFake is an explainable fake news detection system based on content and context-level information.
Emotion Detection for Misinformation: A Review
The emotions and sentiments of netizens, as expressed in social media posts and news, constitute important factors that can help to distinguish fake news from genuine news and to understand the spread of rumors.
ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation
To address this shortcoming, we propose a novel KD approach to GNN compression that we call Attention-Based Knowledge Distillation (ABKD).
COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News
This paper goes beyond by establishing BERT as the superior model for fake news detection and demonstrates its utility as a tool to empower the general populace.
Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting
To break such a dilemma, a feasible but not well-studied solution is to leverage social contexts (e. g., comments) from historical news for training a detection model and apply it to newly emerging news without social contexts.
Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks
Furthermore, SheepDog extracts content-focused veracity attributions from LLMs, where the news content is evaluated against a set of fact-checking rationales.
GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media
Additionally, the pre-trained ResNet-152, as a Convolutional Neural Network (CNN), has been utilized as the image encoder.
