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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 101–150 of 490 papers

TitleStatusHype
The Limitations of Stylometry for Detecting Machine-Generated Fake News—0
Credulous Users and Fake News: a Real Case Study on the Propagation in Twitter—0
A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT—0
A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks—0
A Regularized LSTM Method for Detecting Fake News Articles—0
A Hybrid Attention Framework for Fake News Detection with Large Language Models—0
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment—0
COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News—0
COOL: Comprehensive Knowledge Enhanced Prompt Learning for Domain Adaptive Few-shot Fake News Detection—0
ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification—0
Arabic Fake News Detection Based on Deep Contextualized Embedding Models—0
Constraint 2021: Machine Learning Models for COVID-19 Fake News Detection Shared Task—0
Credibility-based Fake News Detection—0
CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit—0
Connecting the Dots Between Fact Verification and Fake News Detection—0
CroMe: Multimodal Fake News Detection using Cross-Modal Tri-Transformer and Metric Learning—0
Concepts and Experiments on Psychoanalysis Driven Computing—0
A Proposed Bi-LSTM Method to Fake News Detection—0
Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks (Technical Report)—0
Adaptive Learning of Consistency and Inconsistency Information for Fake News Detection—0
Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey—0
Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection—0
A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention—0
A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection—0
Approaches for Improving the Performance of Fake News Detection in Bangla: Imbalance Handling and Model Stacking—0
Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language—0
Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models—0
Evaluating the Efficacy of Large Language Models in Detecting Fake News: A Comparative Analysis—0
Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection—0
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection—0
Annotation-Scheme Reconstruction for “Fake News” and Japanese Fake News Dataset—0
Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models—0
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
Evaluating Deep Learning Approaches for Covid19 Fake News Detection—0
Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media—0
ExFake: Towards an Explainable Fake News Detection Based on Content and Social Context Information—0
Exploring Modality Disruption in Multimodal Fake News Detection—0
Claim extraction from text using transfer learning.—0
Claim Detection in Biomedical Twitter Posts—0
Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset—0
CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection—0
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing—0
Adversarial Examples for Natural Language Classification Problems—0
Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques—0
An exploration of features to improve the generalisability of fake news detection models—0
Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions—0
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It—0
BRENDA: Browser Extension for Fake News Detection—0
BREAKING! Presenting Fake News Corpus for Automated Fact Checking—0
A New cross-domain strategy based XAI models 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