Fake News Detection On Fnc 1

Métriques

Per-class Accuracy (Agree)

Per-class Accuracy (Disagree)

Per-class Accuracy (Discuss)

Per-class Accuracy (Unrelated)

Weighted Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

						Paper Title
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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