Abstract
As of 2020 when the COVID-19 pandemic is full-blown on a global scale, people's need to have access to legitimate information regarding COVID-19 is more urgent than ever, especially via online media where the abundance of irrelevant information overshadows the more informative ones. In response to such, we proposed a model that, given an English tweet, automatically identifies whether that tweet bears informative content regarding COVID-19 or not. By ensembling different BERTweet model configurations, we have achieved competitive results that are only shy of those by top performing teams by roughly 1% in terms of F1 score on the informative class. In the post-competition period, we have also experimented with various other approaches that potentially boost generalization to a new dataset.
Abstract (translated)
URL
https://arxiv.org/abs/2009.06372
