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HyperAI超神经

Fake News Detection On Fnc 1

评估指标

Per-class Accuracy (Agree)

Per-class Accuracy (Disagree)

Per-class Accuracy (Discuss)

Per-class Accuracy (Unrelated)

Weighted Accuracy

评测结果

各个模型在此基准测试上的表现结果

						Paper Title	Repository
Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021)	75.03	63.41	85.97	99.36	90.73	Exploring Summarization to Enhance Headline Stance Detection	-
ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022)	88.47	96.00	87.70	95.04	84.60	Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection	-
Bhatt et al.	43.82	6.31	85.68	98.04	83.08	On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
Bi-LSTM (max-pooling, attention)	51.34	10.33	81.52	96.74	82.23	Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News
3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017)	44.04	6.60	81.38	97.90	81.72	A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Neural method from Mohtarami et al. + TF-IDF (Mohtarami et al., 2018)	-	-	-	-	81.23	Automatic Stance Detection Using End-to-End Memory Networks	-
Neural method from Mohtarami et al. (Mohtarami et al., 2018)	-	-	-	-	78.97	Automatic Stance Detection Using End-to-End Memory Networks	-
Baseline based on skip-thought embeddings (Bhatt et al., 2017)	31.80	0.00	81.20	91.18	76.18	On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
Baseline based on word2vec + hand-crafted features (Bhatt et al., 2017)	50.70	9.61	53.38	96.05	72.78	On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
Neural baseline based on bi-directional LSTMs (Bhatt et al., 2017)	38.04	4.59	58.132	78.27	63.11	On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification

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Fake News Detection On Fnc 1 | SOTA | HyperAI超神经