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
Reliability Estimation of News Media Sources: Birds of a Feather Flock Together
Contrary to previous research, our proposed approach models the problem as the estimation of a reliability degree, and not a reliability label, based on how all the news media sources interact with each other on the Web.
Blessing or curse? A survey on the Impact of Generative AI on Fake News
This survey provides a comprehensive examination of the research and practical use of Generative AI for Fake News detection and creation in 2024.
Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques
We carry out comprehensive trials to show the effectiveness of these models in identifying bogus news in Bangla, with the Bidirectional GRU model having a stunning accuracy of 99. 16%.
FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detecction
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content.
Towards Knowledge-Grounded Natural Language Understanding and Generation
This thesis investigates how natural language understanding and generation with transformer models can benefit from grounding the models with knowledge representations and addresses the following key research questions: (i) Can knowledge of entities extend its benefits beyond entity-centric tasks, such as entity linking?
TT-BLIP: Enhancing Fake News Detection Using BLIP and Tri-Transformer
This paper introduces an end-to-end model called TT-BLIP that applies the bootstrapping language-image pretraining for unified vision-language understanding and generation (BLIP) for three types of information: BERT and BLIP\textsubscript{Txt} for text, ResNet and BLIP\textsubscript{Img} for images, and bidirectional BLIP encoders for multimodal information.
MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection
In addition, various existing Chinese fake news detection methods are thoroughly evaluated on our proposed dataset in cross-source, multi-source, and unseen source ways.
Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors
The proliferation of fake news has had far-reaching implications on politics, the economy, and society at large.
FakeNewsGPT4: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs
In this paper, we propose FakeNewsGPT4, a novel framework that augments Large Vision-Language Models (LVLMs) with forgery-specific knowledge for manipulation reasoning while inheriting extensive world knowledge as complementary.
Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media
With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society.
