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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 121–130 of 490 papers

Title	Date	Tasks	Status
Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey	Jan 20, 2022	Fact CheckingFake News Detection	—Unverified
Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection	Oct 15, 2022	Fake News Detection	—Unverified
A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection	Mar 26, 2022	Fake News DetectionGraph Neural Network	—Unverified
Approaches for Improving the Performance of Fake News Detection in Bangla: Imbalance Handling and Model Stacking	Mar 22, 2022	Fake News Detection	—Unverified
A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN	Dec 1, 2020	Explainable ModelsFake News Detection	—Unverified
Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language	May 1, 2020	ArticlesData Augmentation	—Unverified
Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models	Mar 1, 2021	Fact CheckingFake News Detection	—Unverified
Detection of fake news on CoViD-19 on Web Search Engines	Mar 22, 2021	ArticlesFake News Detection	—Unverified
Development of Fake News Model using Machine Learning through Natural Language Processing	Jan 19, 2022	BIG-bench Machine LearningClassification	—Unverified
Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection	Mar 27, 2025	Fake News DetectionIn-Context Learning	—Unverified

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All datasets FNC-1 RAWFC Grover-Mega LIAR Hostility Detection Dataset in Hindi COVID-19 Fake News Dataset MediaEval2016 PolitiFact Social media Weibo NER

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021)	Weighted Accuracy	90.73	—	Unverified
2	ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022)	Weighted Accuracy	84.6	—	Unverified
3	Bhatt et al.	Weighted Accuracy	83.08	—	Unverified
4	Bi-LSTM (max-pooling, attention)	Weighted Accuracy	82.23	—	Unverified
5	3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017)	Weighted Accuracy	81.72	—	Unverified
6	Neural method from Mohtarami et al. + TF-IDF (Mohtarami et al., 2018)	Weighted Accuracy	81.23	—	Unverified
7	Neural method from Mohtarami et al. (Mohtarami et al., 2018)	Weighted Accuracy	78.97	—	Unverified
8	Baseline based on skip-thought embeddings (Bhatt et al., 2017)	Weighted Accuracy	76.18	—	Unverified
9	Baseline based on word2vec + hand-crafted features (Bhatt et al., 2017)	Weighted Accuracy	72.78	—	Unverified
10	Neural baseline based on bi-directional LSTMs (Bhatt et al., 2017)	Weighted Accuracy	63.11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Persuasive Writing Strategy	F1	55.8	—	Unverified
2	HiSS	F1	53.9	—	Unverified
3	CofCED	F1	51.1	—	Unverified
4	ReAct	F1	49.8	—	Unverified
5	Standard prompting with articles	F1	47.9	—	Unverified
6	CoT	F1	44.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Text-Transformers + Five-fold five model cross-validation +Pseudo Label Algorithm	Unpaired Accuracy	98.5	—	Unverified
2	Grover-Mega	Unpaired Accuracy	92	—	Unverified
3	Grover-Large	Unpaired Accuracy	80.8	—	Unverified
4	BERT-Large	Unpaired Accuracy	73.1	—	Unverified
5	GPT2 (355M)	Unpaired Accuracy	70.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Hybrid CNNs (Text + All)	Test Accuracy	0.27	—	Unverified
2	CNNs	Test Accuracy	0.27	—	Unverified
3	Hybrid CNNs (Text + Speaker)	Test Accuracy	0.25	—	Unverified
4	Bi-LSTMs	Test Accuracy	0.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Auxiliary IndicBert	F1 score	0.77	—	Unverified
2	Auxiliary IndicBert	F1 score	0.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Ensemble Model + Heuristic Post-Processing	F1	0.99	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SEMI-FND	Accuracy	85.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Convolutional Tsetlin Machine	1:1 Accuracy	91.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TextRNN	Accuracy	92.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SEMI-FND	Accuracy	86.83	—	Unverified