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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 490 papers

TitleStatusHype
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models—0
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection—0
Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection—0
Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models—0
Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection—0
Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey—0
Concepts and Experiments on Psychoanalysis Driven Computing—0
Connecting the Dots Between Fact Verification and Fake News Detection—0
Constraint 2021: Machine Learning Models for COVID-19 Fake News Detection Shared Task—0
ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification—0
COOL: Comprehensive Knowledge Enhanced Prompt Learning for Domain Adaptive Few-shot Fake News Detection—0
COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News—0
Credibility-based Fake News Detection—0
CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit—0
Credulous Users and Fake News: a Real Case Study on the Propagation in Twitter—0
CroMe: Multimodal Fake News Detection using Cross-Modal Tri-Transformer and Metric Learning—0
Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language—0
Dataset of Fake News Detection and Fact Verification: A Survey—0
DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection—0
Debunking Disinformation: Revolutionizing Truth with NLP in Fake News Detection—0
Deception Detection in News Reports in the Russian Language: Lexics and Discourse—0
Deconfounded Reasoning for Multimodal Fake News Detection via Causal Intervention—0
Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF—0
Detecting fake news by enhanced text representation with multi-EDU-structure awareness—0
Detecting Fake News with Capsule Neural Networks—0
Detecting Fake News with Weak Social Supervision—0
Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model—0
Detect, Investigate, Judge and Determine: A Novel LLM-based Framework for Few-shot Fake News Detection—0
Detection of fake news on CoViD-19 on Web Search Engines—0
KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection—0
Label Noise-Resistant Mean Teaching for Weakly Supervised Fake News Detection—0
Large Language Model Agent for Fake News Detection—0
Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection—0
Less is More: Unseen Domain Fake News Detection via Causal Propagation Substructures—0
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News—0
Leveraging Selective Prediction for Reliable Image Geolocation—0
Leveraging Users' Social Network Embeddings for Fake News Detection on Twitter—0
LEX-GAN: Layered Explainable Rumor Detector Based on Generative Adversarial Networks—0
Lexicon generation for detecting fake news—0
Lifelong Evolution: Collaborative Learning between Large and Small Language Models for Continuous Emergent Fake News Detection—0
Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification—0
LingML: Linguistic-Informed Machine Learning for Enhanced Fake News Detection—0
LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection—0
LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection—0
Machine Learning Approach to Fact-Checking in West Slavic Languages—0
Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News—0
Machine Learning Explanations to Prevent Overtrust in Fake News Detection—0
Machine Learning Technique Based Fake News Detection—0
MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection—0
Show:102550
← PrevPage 5 of 10Next →
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