BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models
This work addresses the need for accurate information filtering on social media during the COVID-19 pandemic, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of identifying informative COVID-19 tweets from a dataset of 10,000 English tweets, achieving an F1 score of 88.81% for the informative label on the test set using an ensemble of transformer and deep learning models.
The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81% on the test set.