Papers › Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble
Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble
Giorgos Tziafas, Konstantinos Kogkalidis, Tommaso Caselli
This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English. We fine tune each model on each of the task's questions and aggregate their prediction scores using a majority voting approach. TOKOFOU obtains an overall F1 score of 89.7%, ranking first.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Misinformation | NLP4IF-2021--Fighting the COVID-19 Infodemic | TOKOFOU | Average F1 | 89.7 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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