Exploring Summarization to Enhance Headline Stance Detection | ✓ Link | 90.73 | 99.36 | 75.03 | 63.41 | 85.97 | Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021) | 2021-06-20 |
Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection | | 84.60 | 95.04 | 88.47 | 96.00 | 87.70 | ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022) | 2022-10-15 |
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification | ✓ Link | 83.08 | 98.04 | 43.82 | 6.31 | 85.68 | Bhatt et al. | 2017-12-11 |
Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News | ✓ Link | 82.23 | 96.74 | 51.34 | 10.33 | 81.52 | Bi-LSTM (max-pooling, attention) | 2018-11-02 |
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task | ✓ Link | 81.72 | 97.90 | 44.04 | 6.60 | 81.38 | 3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017) | 2017-07-11 |
Automatic Stance Detection Using End-to-End Memory Networks | | 81.23 | | | | | Neural method from Mohtarami et al. + TF-IDF (Mohtarami et al., 2018) | 2018-04-20 |
Automatic Stance Detection Using End-to-End Memory Networks | | 78.97 | | | | | Neural method from Mohtarami et al. (Mohtarami et al., 2018) | 2018-04-20 |
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification | ✓ Link | 76.18 | 91.18 | 31.80 | 0.00 | 81.20 | Baseline based on skip-thought embeddings (Bhatt et al., 2017) | 2017-12-11 |
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification | ✓ Link | 72.78 | 96.05 | 50.70 | 9.61 | 53.38 | Baseline based on word2vec + hand-crafted features (Bhatt et al., 2017) | 2017-12-11 |
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification | ✓ Link | 63.11 | 78.27 | 38.04 | 4.59 | 58.132 | Neural baseline based on bi-directional LSTMs (Bhatt et al., 2017) | 2017-12-11 |