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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 301–325 of 490 papers

TitleStatusHype
Multimodal Hate Speech Detection from Bengali Memes and TextsCode0
Automatic Fake News Detection: Are current models "fact-checking" or "gut-checking"?—0
Methods of Informational Trends Analytics and Fake News Detection on Twitter—0
Fake news detection using parallel BERT deep neural networks—0
Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset—0
The 2021 Urdu Fake News Detection Task using Supervised Machine Learning and Feature Combinations—0
Applying Automatic Text Summarization for Fake News DetectionCode0
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
Fake News Detection Using Majority Voting Technique—0
Domain Adaptive Fake News Detection via Reinforcement Learning—0
A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model—0
Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey—0
Development of Fake News Model using Machine Learning through Natural Language Processing—0
GREENER: Graph Neural Networks for News Media Profiling—0
Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection—0
An Adversarial Benchmark for Fake News Detection ModelsCode0
Testing the Robustness of a BiLSTM-based Structural Story Classifier—0
Automated Fake News Detection using cross-checking with reliable sources—0
Automated Evidence Collection for Fake News DetectionCode0
Multimodal Fake News Detection—0
Studies Towards Language Independent Fake News Detection—0
How Deep Are the Fakes? Focusing on Audio Deepfake: A Survey—0
Leveraging Selective Prediction for Reliable Image Geolocation—0
A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks—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