Fake News Detection on FNC-1

Metric: Per-class Accuracy (Unrelated) (higher is better)

LeaderboardDataset

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#	Model↕	Per-class Accuracy (Unrelated)▼	Extra Data	Paper	Date↕	Code
1	Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021)	99.36	No	-	-	Code
2	Bhatt et al.	98.04	No	On the Benefit of Combining Neural, Statistical ...	2017-12-11	Code
3	3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017)	97.9	No	A simple but tough-to-beat baseline for the Fake...	2017-07-11	Code
4	Bi-LSTM (max-pooling, attention)	96.74	No	Combining Similarity Features and Deep Represent...	2018-11-02	Code
5	Baseline based on word2vec + hand-crafted features (Bhatt et al., 2017)	96.05	No	On the Benefit of Combining Neural, Statistical ...	2017-12-11	Code
6	ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022)	95.04	No	Combination Of Convolution Neural Networks And D...	2022-10-15	-
7	Baseline based on skip-thought embeddings (Bhatt et al., 2017)	91.18	No	On the Benefit of Combining Neural, Statistical ...	2017-12-11	Code
8	Neural baseline based on bi-directional LSTMs (Bhatt et al., 2017)	78.27	Yes	On the Benefit of Combining Neural, Statistical ...	2017-12-11	Code

#1Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021)
99.36
Per-class Accuracy (Unrelated)
No paperCode
#2Bhatt et al.SOTA
98.04
Per-class Accuracy (Unrelated)· 2017-12-11
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification Code
#33rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017)SOTA
97.9
Per-class Accuracy (Unrelated)· 2017-07-11
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task Code
#4Bi-LSTM (max-pooling, attention)
96.74
Per-class Accuracy (Unrelated)· 2018-11-02
Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News Code
#5Baseline based on word2vec + hand-crafted features (Bhatt et al., 2017)
96.05
Per-class Accuracy (Unrelated)· 2017-12-11
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification Code
#6ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022)
95.04
Per-class Accuracy (Unrelated)· 2022-10-15
Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection
#7Baseline based on skip-thought embeddings (Bhatt et al., 2017)
91.18
Per-class Accuracy (Unrelated)· 2017-12-11
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification Code
#8Neural baseline based on bi-directional LSTMs (Bhatt et al., 2017)
78.27
Per-class Accuracy (Unrelated)· Extra Data· 2017-12-11
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification Code