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 451–490 of 490 papers

TitleStatusHype
Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the WebCode0
Applications of Social Media in Hydroinformatics: A Survey—0
The Role of User Profile for Fake News Detection—0
Fake News Early Detection: An Interdisciplinary Study—0
Open Issues in Combating Fake News: Interpretability as an Opportunity—0
Neural Abstractive Text Summarization and Fake News Detection—0
Mining Dual Emotion for Fake News DetectionCode1
Learning Hierarchical Discourse-level Structure for Fake News DetectionCode0
Fake News Detection on Social Media using Geometric Deep LearningCode1
Fake News Detection via NLP is Vulnerable to Adversarial AttacksCode0
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical EncoderCode0
A Deep Ensemble Framework for Fake News Detection and Classification—0
A Survey on Natural Language Processing for Fake News DetectionCode0
Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles—0
The Data Challenge in Misinformation Detection: Source Reputation vs. Content VeracityCode0
An End-to-End Multi-task Learning Model for Fact Checking—0
A mostly unlexicalized model for recognizing textual entailment—0
CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection—0
Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the WebCode0
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News DetectionCode0
Debunking Fake News One Feature at a TimeCode0
Multi-Source Multi-Class Fake News Detection—0
Stance-In-Depth Deep Neural Approach to Stance Classification—0
TI-CNN: Convolutional Neural Networks for Fake News DetectionCode0
FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural NetworkCode0
Adversarial Examples for Natural Language Classification Problems—0
On the Benefit of Combining Neural, Statistical and External Features for Fake News IdentificationCode0
Fake News Detection Through Multi-Perspective Speaker Profiles—0
A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based FeaturesCode0
Fake news stance detection using stacked ensemble of classifiers—0
From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles—0
Deception Detection in News Reports in the Russian Language: Lexics and Discourse—0
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking—0
Automatic Detection of Fake News—0
Fake News Detection on Social Media: A Data Mining PerspectiveCode2
``Liar, Liar Pants on Fire'': A New Benchmark Dataset for Fake News DetectionCode1
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News DetectionCode1
Some Like it Hoax: Automated Fake News Detection in Social NetworksCode0
CSI: A Hybrid Deep Model for Fake News DetectionCode0
A Stylometric Inquiry into Hyperpartisan and Fake NewsCode0
Show:102550
← PrevPage 10 of 10Next →
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