A deep multi-view imbalanced learning approach for identifying informative COVID-19 tweets from social media.

Social media platforms such as Twitter are home ground for rapid COVID-19-related information sharing over the Internet, thereby becoming the favorable data resource for many downstream applications. Due to the massive pile of COVID-19 tweets generated every day, it is significant that the machine-learning-supported downstream applications can effectively skip the uninformative tweets and only pick up the informative tweets for their further use. However, existing solutions do not specifically consider the negative effect caused by the imbalanced ratios between informative and uninformative tweets in training data.