g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection
arXiv:2012.11967 · doi:10.1007/978-3-030-73696-5_12
Abstract
The COVID-19 pandemic has had a huge impact on various areas of human life. Hence, the coronavirus pandemic and its consequences are being actively discussed on social media. However, not all social media posts are truthful. Many of them spread fake news that cause panic among readers, misinform people and thus exacerbate the effect of the pandemic. In this paper, we present our results at the Constraint@AAAI2021 Shared Task: COVID-19 Fake News Detection in English. In particular, we propose our approach using the transformer-based ensemble of COVID-Twitter-BERT (CT-BERT) models. We describe the models used, the ways of text preprocessing and adding extra data. As a result, our best model achieved the weighted F1-score of 98.69 on the test set (the first place in the leaderboard) of this shared task that attracted 166 submitted teams in total.
arXiv commentThe winning solution at the Constraint shared task (AAAI-2021)
References in corpus (9)
- Scikit-learn: Machine Learning in Python
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Decoupled Weight Decay Regularization
- Fighting an Infodemic: COVID-19 Fake News Dataset
- CoAID: COVID-19 Healthcare Misinformation Dataset
- Analysing user identity via time-sensitive semantic edit distance (t-SED): A case study of Russian trolls on Twitter
- Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic
- Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models
- QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions