  Amid the pandemic COVID-19, the world is facing unprecedented infodemic with
the proliferation of both fake and real information. Considering the
problematic consequences that the COVID-19 fake-news have brought, the
scientific community has put effort to tackle it. To contribute to this fight
against the infodemic, we aim to achieve a robust model for the COVID-19
fake-news detection task proposed at CONSTRAINT 2021 (FakeNews-19) by taking
two separate approaches: 1) fine-tuning transformers based language models with
robust loss functions and 2) removing harmful training instances through
influence calculation. We further evaluate the robustness of our models by
evaluating on different COVID-19 misinformation test set (Tweets-19) to
understand model generalization ability. With the first approach, we achieve
98.13% for weighted F1 score (W-F1) for the shared task, whereas 38.18% W-F1 on
the Tweets-19 highest. On the contrary, by performing influence data cleansing,
our model with 99% cleansing percentage can achieve 54.33% W-F1 score on
Tweets-19 with a trade-off. By evaluating our models on two COVID-19 fake-news
test sets, we suggest the importance of model generalization ability in this
task to step forward to tackle the COVID-19 fake-news problem in online social
media platforms.
