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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 51–100 of 490 papers

TitleStatusHype
A Generalized Deep Markov Random Fields Framework for Fake News Detection.Code1
A Python Tool for Reconstructing Full News Text from GDELTCode1
A Heuristic-driven Ensemble Framework for COVID-19 Fake News DetectionCode1
AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection DatasetCode1
A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News ArticlesCode1
Cross-lingual COVID-19 Fake News DetectionCode1
3HAN: A Deep Neural Network for Fake News DetectionCode1
Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting MethodCode1
User Preference-aware Fake News DetectionCode1
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake NewsCode1
SAFE: Similarity-Aware Multi-Modal Fake News DetectionCode1
Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News DetectionCode1
Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet GraphCode1
DAAD: Dynamic Analysis and Adaptive Discriminator for Fake News DetectionCode1
Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News DetectionCode1
A Survey on Interpretable Cross-modal ReasoningCode1
AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News DetectionCode1
Detecting Deepfakes Without Seeing AnyCode1
Dual-Teacher De-biasing Distillation Framework for Multi-domain Fake News DetectionCode1
Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News DetectionCode1
Cross-Modal Augmentation for Few-Shot Multimodal Fake News DetectionCode1
Evaluation of Fake News Detection with Knowledge-Enhanced Language ModelsCode1
Mining Dual Emotion for Fake News DetectionCode1
Exploring Fake News Detection with Heterogeneous Social Media Context GraphsCode1
Cross-modal Contrastive Learning for Multimodal Fake News DetectionCode1
Entity-Aware Dual Co-Attention Network for Fake News DetectionCode1
Automatic Fake News Detection: Are Models Learning to Reason?Code1
Fake news detection: Taxonomy and comparative studyCode1
Adapting Fake News Detection to the Era of Large Language ModelsCode1
Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News DetectionCode1
BanFakeNews: A Dataset for Detecting Fake News in BanglaCode1
FANG: Leveraging Social Context for Fake News Detection Using Graph RepresentationCode1
An Enhanced Fake News Detection System With Fuzzy Deep LearningCode1
GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News DetectionCode1
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural NetworksCode1
Generalizing to the Future: Mitigating Entity Bias in Fake News DetectionCode1
Hierarchical Multi-head Attentive Network for Evidence-aware Fake News DetectionCode1
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level TransferCode1
Integrating Pattern- and Fact-based Fake News Detection via Model Preference LearningCode1
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News DetectionCode1
Multiverse: Multilingual Evidence for Fake News DetectionCode1
LTCR: Long-Text Chinese Rumor Detection DatasetCode1
Memory-Guided Multi-View Multi-Domain Fake News DetectionCode1
A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN—0
A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention—0
A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements—0
A Self-Learning Multimodal Approach for Fake News Detection—0
Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks (Technical Report)—0
A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit—0
A Deep Ensemble Framework for Fake News Detection and Classification—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