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
Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom
To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework.
Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection
In this paper, we curate and contribute the first largest publicly available dataset for Urdu FND, Ax-to-Grind Urdu, to bridge the identified gaps and limitations of existing Urdu datasets in the literature.
Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations
dogregor/pretrain_gnns_fakenewsnet
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Pre-training of neural networks has recently revolutionized the field of Natural Language Processing (NLP) and has before demonstrated its effectiveness in computer vision.
TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
less-and-less-bugs/trust_teller
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The proliferation of fake news has emerged as a severe societal problem, raising significant interest from industry and academia.
FaKnow: A Unified Library for Fake News Detection
npurg/faknow
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Over the past years, a large number of fake news detection algorithms based on deep learning have emerged.
Fuzzy Deep Hybrid Network for Fake News Detection
chengxuphd/FDHN
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Proceedings of the 12th International Symposium on Information and Communication Technology 2023
In this paper, we propose an innovative fuzzy logic-based hybrid model to improve the performance of fake news detection.
Dual-Teacher De-biasing Distillation Framework for Multi-domain Fake News Detection
ningljy/dtdbd
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In particular, the DTDBD consists of an unbiased teacher and a clean teacher that jointly guide the student model in mitigating domain bias and maintaining performance.
BanMANI: A Dataset to Identify Manipulated Social Media News in Bangla
Initial work has been done to address fake news detection and misrepresentation of news in the Bengali language.
Detecting Deepfakes Without Seeing Any
talreiss/factor
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We therefore introduce the concept of "fact checking", adapted from fake news detection, for detecting zero-day deepfake attacks.
Adapting Fake News Detection to the Era of Large Language Models
With the proliferation of both human-written and machine-generated real and fake news, robustly and effectively discerning the veracity of news articles has become an intricate challenge.
