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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 351–400 of 490 papers

TitleStatusHype
MMCFND: Multimodal Multilingual Caption-aware Fake News Detection for Low-resource Indic Languages—0
Adaptation Method for Misinformation Identification—0
MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classification—0
MMNet: Multimodal Fusion via Mutual Learning Network for Fake News Detection—0
Multimodal Matching-aware Co-attention Networks with Mutual Knowledge Distillation for Fake News Detection—0
An End-to-End Multi-task Learning Model for Fact Checking—0
Model Generalization on COVID-19 Fake News Detection—0
Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach—0
Two-path Deep Semi-supervised Learning for Timely Fake News Detection—0
MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation—0
MSynFD: Multi-hop Syntax aware Fake News Detection—0
MTPareto: A MultiModal Targeted Pareto Framework for Fake News Detection—0
Multimodal Detection of Information Disorder from Social Media—0
Multimodal Emergent Fake News Detection via Meta Neural Process Networks—0
Multimodal Fake News Detection—0
An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning—0
Multi-modal Fake News Detection on Social Media via Multi-grained Information Fusion—0
Multimodal Fake News Detection via CLIP-Guided Learning—0
An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning—0
Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model—0
Multimodal Fusion with Co-Attention Networks for Fake News Detection—0
ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation—0
Multimodal Inverse Attention Network with Intrinsic Discriminant Feature Exploitation for Fake News Detection—0
Multi-Source Multi-Class Fake News Detection—0
Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection—0
A Multi-Policy Framework for Deep Learning-Based Fake News Detection—0
Words are the Window to the Soul: Language-based User Representations for Fake News Detection—0
Multi-view Fake News Detection Model Based on Dynamic Hypergraph—0
A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model—0
Neural Abstractive Text Summarization and Fake News Detection—0
News about Global North considered Truthful! The Geo-political Veracity Gradient in Global South News—0
NexusIndex: Integrating Advanced Vector Indexing and Multi-Model Embeddings for Robust Fake News Detection—0
NoFake at CheckThat! 2021: Fake News Detection Using BERT—0
No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation—0
A Multimodal Adaptive Graph-based Intelligent Classification Model for Fake News—0
A mostly unlexicalized model for recognizing textual entailment—0
Official-NV: An LLM-Generated News Video Dataset for Multimodal Fake News Detection—0
UNITE-FND: Reframing Multimodal Fake News Detection through Unimodal Scene Translation—0
Unseen Fake News Detection Through Casual Debiasing—0
Open Issues in Combating Fake News: Interpretability as an Opportunity—0
Orwellian-times at SemEval-2019 Task 4: A Stylistic and Content-based Classifier—0
Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing—0
Overview of Factify5WQA: Fact Verification through 5W Question-Answering—0
Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021—0
Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020—0
Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals—0
POLygraph: Polish Fake News Dataset—0
A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection—0
A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN—0
RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection—0
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All datasetsFNC-1RAWFCGrover-MegaLIARHostility Detection Dataset in HindiCOVID-19 Fake News DatasetMediaEval2016PolitiFactSocial mediaWeibo NER

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021)Weighted Accuracy90.73—Unverified
2ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022)Weighted Accuracy84.6—Unverified
3Bhatt et al.Weighted Accuracy83.08—Unverified
4Bi-LSTM (max-pooling, attention)Weighted Accuracy82.23—Unverified
53rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017)Weighted Accuracy81.72—Unverified
6Neural method from Mohtarami et al. + TF-IDF (Mohtarami et al., 2018)Weighted Accuracy81.23—Unverified
7Neural method from Mohtarami et al. (Mohtarami et al., 2018)Weighted Accuracy78.97—Unverified
8Baseline based on skip-thought embeddings (Bhatt et al., 2017)Weighted Accuracy76.18—Unverified
9Baseline based on word2vec + hand-crafted features (Bhatt et al., 2017)Weighted Accuracy72.78—Unverified
10Neural baseline based on bi-directional LSTMs (Bhatt et al., 2017)Weighted Accuracy63.11—Unverified
#ModelMetricClaimedVerifiedStatus
1Persuasive Writing StrategyF155.8—Unverified
2HiSSF153.9—Unverified
3CofCEDF151.1—Unverified
4ReActF149.8—Unverified
5Standard prompting with articlesF147.9—Unverified
6CoTF144.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Text-Transformers + Five-fold five model cross-validation +Pseudo Label AlgorithmUnpaired Accuracy98.5—Unverified
2Grover-MegaUnpaired Accuracy92—Unverified
3Grover-LargeUnpaired Accuracy80.8—Unverified
4BERT-LargeUnpaired Accuracy73.1—Unverified
5GPT2 (355M)Unpaired Accuracy70.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid CNNs (Text + All)Test Accuracy0.27—Unverified
2CNNsTest Accuracy0.27—Unverified
3Hybrid CNNs (Text + Speaker)Test Accuracy0.25—Unverified
4Bi-LSTMsTest Accuracy0.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Auxiliary IndicBertF1 score0.77—Unverified
2Auxiliary IndicBertF1 score0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Ensemble Model + Heuristic Post-ProcessingF10.99—Unverified
#ModelMetricClaimedVerifiedStatus
1SEMI-FNDAccuracy85.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Convolutional Tsetlin Machine1:1 Accuracy91.21—Unverified
#ModelMetricClaimedVerifiedStatus
1TextRNNAccuracy92.4—Unverified
#ModelMetricClaimedVerifiedStatus
1SEMI-FNDAccuracy86.83—Unverified