Detection of COVID-19 informative tweets using RoBERTa
This work addresses the need to filter relevant information during the COVID-19 pandemic for public health monitoring, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled the problem of detecting informative COVID-19 tweets from social media data, achieving an F1-score of 0.89 on validation and 0.87 on a leaderboard using the RoBERTa model.
Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.