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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–490 of 490 papers

TitleStatusHype
Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation—0
Emulating Reader Behaviors for Fake News Detection—0
End-to-end argumentation knowledge graph construction—0
Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques—0
SGG: Spinbot, Grammarly and GloVe based Fake News Detection—0
ClaimTrust: Propagation Trust Scoring for RAG Systems—0
SCG: Spotting Coordinated Groups in Social Media—0
Similarity-Aware Multimodal Prompt Learning for Fake News Detection—0
Claim extraction from text using transfer learning.—0
Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI—0
Evaluating Deep Learning Approaches for Covid19 Fake News Detection—0
Evaluating the Efficacy of Large Language Models in Detecting Fake News: A Comparative Analysis—0
Claim Detection in Biomedical Twitter Posts—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
CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection—0
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment—0
Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions—0
Exploiting Multi-domain Visual Information for Fake News Detection—0
Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting—0
BRENDA: Browser Extension for Fake News Detection—0
Similarity Detection Pipeline for Crawling a Topic Related Fake News Corpus—0
Exploring Modality Disruption in Multimodal Fake News Detection—0
SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?—0
BREAKING! Presenting Fake News Corpus for Automated Fact Checking—0
Exploring Text Representations for Online Misinformation—0
Blessing or curse? A survey on the Impact of Generative AI on Fake News—0
External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection—0
Fact-checking based fake news detection: a review—0
Veritas Annotator: Discovering the Origin of a Rumour—0
Factorization of Fact-Checks for Low Resource Indian Languages—0
Fake Advertisements Detection Using Automated Multimodal Learning: A Case Study for Vietnamese Real Estate Data—0
Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection—0
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It—0
Fake News and Phishing Detection Using a Machine Learning Trained Expert System—0
fakenewsbr: A Fake News Detection Platform for Brazilian Portuguese—0
Fake News Data Collection and Classification: Iterative Query Selection for Opaque Search Engines with Pseudo Relevance Feedback—0
Stance Detection in German News Articles—0
Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media—0
Fake News Detection and Behavioral Analysis: Case of COVID-19—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