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

Showing 401450 of 490 papers

TitleStatusHype
Connecting the Dots Between Fact Verification and Fake News Detection0
A Review on Fact Extraction and VerificationCode0
SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detectionCode0
Fake News Spreader Detection on Twitter using Character N-Grams. Notebook for PAN at CLEF 20200
Similarity Detection Pipeline for Crawling a Topic Related Fake News Corpus0
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection ModelsCode0
SGG: Spinbot, Grammarly and GloVe based Fake News Detection0
Weighted Accuracy Algorithmic Approach In Counteracting Fake News And Disinformation0
Machine Learning Explanations to Prevent Overtrust in Fake News Detection0
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language TasksCode0
Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks0
BRENDA: Browser Extension for Fake News Detection0
A Deep Learning Approach for Automatic Detection of Fake NewsCode0
Credulous Users and Fake News: a Real Case Study on the Propagation in Twitter0
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing0
French Tweet Corpus for Automatic Stance Detection0
Measuring the Impact of Readability Features in Fake News Detection0
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection0
Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection0
Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language0
Adaptive Interaction Fusion Networks for Fake News Detection0
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News0
Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks0
Fake News Detection with Different Models0
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention0
Fake News Detection by means of Uncertainty Weighted Causal GraphsCode0
Detecting Fake News with Capsule Neural Networks0
Two-path Deep Semi-supervised Learning for Timely Fake News Detection0
To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers0
Weak Supervision for Fake News Detection via Reinforcement LearningCode0
A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements0
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News DetectionCode0
Credibility-based Fake News Detection0
Veritas Annotator: Discovering the Origin of a Rumour0
Detecting Fake News with Weak Social Supervision0
Localization of Fake News Detection via Multitask Transfer LearningCode0
SCG: Spotting Coordinated Groups in Social Media0
Learning from Fact-checkers: Analysis and Generation of Fact-checking LanguageCode0
Fake news detection using Deep LearningCode0
LEX-GAN: Layered Explainable Rumor Detector Based on Generative Adversarial Networks0
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection0
Machine Learning Approach to Fact-Checking in West Slavic Languages0
The Limitations of Stylometry for Detecting Machine-Generated Fake News0
Exploiting Multi-domain Visual Information for Fake News Detection0
Tensor Factorization with Label Information for Fake News DetectionCode0
Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection0
Fake News Detection as Natural Language InferenceCode0
BREAKING! Presenting Fake News Corpus for Automated Fact Checking0
Fake News Detection using Stance Classification: A Survey0
Deep Two-path Semi-supervised Learning for Fake News DetectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021)Weighted Accuracy90.73Unverified
2ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022)Weighted Accuracy84.6Unverified
3Bhatt et al.Weighted Accuracy83.08Unverified
4Bi-LSTM (max-pooling, attention)Weighted Accuracy82.23Unverified
53rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017)Weighted Accuracy81.72Unverified
6Neural method from Mohtarami et al. + TF-IDF (Mohtarami et al., 2018)Weighted Accuracy81.23Unverified
7Neural method from Mohtarami et al. (Mohtarami et al., 2018)Weighted Accuracy78.97Unverified
8Baseline based on skip-thought embeddings (Bhatt et al., 2017)Weighted Accuracy76.18Unverified
9Baseline based on word2vec + hand-crafted features (Bhatt et al., 2017)Weighted Accuracy72.78Unverified
10Neural baseline based on bi-directional LSTMs (Bhatt et al., 2017)Weighted Accuracy63.11Unverified
#ModelMetricClaimedVerifiedStatus
1Persuasive Writing StrategyF155.8Unverified
2HiSSF153.9Unverified
3CofCEDF151.1Unverified
4ReActF149.8Unverified
5Standard prompting with articlesF147.9Unverified
6CoTF144.4Unverified
#ModelMetricClaimedVerifiedStatus
1Text-Transformers + Five-fold five model cross-validation +Pseudo Label AlgorithmUnpaired Accuracy98.5Unverified
2Grover-MegaUnpaired Accuracy92Unverified
3Grover-LargeUnpaired Accuracy80.8Unverified
4BERT-LargeUnpaired Accuracy73.1Unverified
5GPT2 (355M)Unpaired Accuracy70.1Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid CNNs (Text + All)Test Accuracy0.27Unverified
2CNNsTest Accuracy0.27Unverified
3Hybrid CNNs (Text + Speaker)Test Accuracy0.25Unverified
4Bi-LSTMsTest Accuracy0.23Unverified
#ModelMetricClaimedVerifiedStatus
1Auxiliary IndicBertF1 score0.77Unverified
2Auxiliary IndicBertF1 score0.57Unverified
#ModelMetricClaimedVerifiedStatus
1Ensemble Model + Heuristic Post-ProcessingF10.99Unverified
#ModelMetricClaimedVerifiedStatus
1SEMI-FNDAccuracy85.8Unverified
#ModelMetricClaimedVerifiedStatus
1Convolutional Tsetlin Machine1:1 Accuracy91.21Unverified
#ModelMetricClaimedVerifiedStatus
1TextRNNAccuracy92.4Unverified
#ModelMetricClaimedVerifiedStatus
1SEMI-FNDAccuracy86.83Unverified