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 251–275 of 490 papers

TitleStatusHype
FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features—0
French Tweet Corpus for Automatic Stance Detection—0
From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles—0
From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection—0
Vision-Language Models Struggle to Align Entities across Modalities—0
Testing the Robustness of a BiLSTM-based Structural Story Classifier—0
Text Classification using Graph Convolutional Networks: A Comprehensive Survey—0
A Systematic Review on the Detection of Fake News Articles—0
The 2021 Urdu Fake News Detection Task using Supervised Machine Learning and Feature Combinations—0
A Survey on Predicting the Factuality and the Bias of News Media—0
A Survey on Automatic Credibility Assessment of Textual Credibility Signals in the Era of Large Language Models—0
A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention—0
Generating artificial texts as substitution or complement of training data—0
VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos—0
A Self-Learning Multimodal Approach for Fake News Detection—0
Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection—0
GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media—0
Graph-based Fake News Detection using a Summarization Technique—0
Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks (Technical Report)—0
The Limitations of Stylometry for Detecting Machine-Generated Fake News—0
Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection—0
GREENER: Graph Neural Networks for News Media Profiling—0
GREENER: Graph Neural Networks for News Media Profiling—0
An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models—0
Health Misinformation Detection in Web Content via Web2Vec: A Structural-, Content-based, and Context-aware Approach based on Web2Vec—0
Show:102550
← PrevPage 11 of 20Next →
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