SOTAVerified

Fake News Detection

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.

Papers

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Showing 1–10 of 490 papers

TitleStatusHype
DCR: Quantifying Data Contamination in LLMs EvaluationCode0
KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection—0
Lifelong Evolution: Collaborative Learning between Large and Small Language Models for Continuous Emergent Fake News Detection—0
Synergizing LLMs with Global Label Propagation for Multimodal Fake News DetectionCode1
Interpretable Graph Learning Over Sets of Temporally-Sparse Data—0
Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla—0
KGAlign: Joint Semantic-Structural Knowledge Encoding for Multimodal Fake News DetectionCode0
MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation—0
The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News—0
Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask LearningCode1
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All datasetsFNC-1RAWFCGrover-MegaLIARHostility Detection Dataset in HindiCOVID-19 Fake News DatasetMediaEval2016PolitiFactSocial mediaWeibo NER

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SEMI-FNDAccuracy86.83—Unverified