Deepluenza: Deep learning for influenza detection from Twitter

Abstract

Influenza is a common seasonal disease that affects people worldwide. Quick reporting methods are needed to detect sudden influenza outbreaks so that health authorities can respond swiftly. Using social media posts to detect influenza related tweets may provide early insights about influenza outbreaks. In this paper, we introduce Deepluenza, a deep learning model to accurately identify influenza reporting tweets. Deepluenza supports multi-language (English and Arabic) Twitter streams. We conducted extensive experiments and compared the results obtained from Deepfluenza with real-life influenza related data collected by health authorities. Our experiments showed that Deepluenza, which is based on the BERT base multilingual model, achieved 0.99 accuracy and F1-score of 0.98 for the influenza reporting class, outperforming other conventional methods. The application of the developed model showed a positive correlation in the number of reports identified from social media with the number of actual hospital visits related to influenza. Furthermore, our experiments showed that combining tweets in different languages, such as English and Arabic, leads to an improved correlation between the number of posted tweets and the number of people’s visits to hospitals due to influenza infections. Deepluenza has the potential to be used for early detection of influenza outbreaks.