Evaluation of IBM Watson Natural Language Processing Service to predict influenza-like illness outbreaks from Twitter data

Abstract

In this work we evaluate whether Watson NLP service can be used to reliably predict infectious disease such as influenza-like illness (ILI) outbreaks using Twitter data. Watson’s performance is evaluated by computing Pearson correlation coefficient between the number of tweets classified by Watson as ILI and the number of ILI occurrences recovered from traditional epidemic surveillance system of the Centers for Disease Control and Prevention (CDC). Achieved correlation was 0.55. Furthermore, a 12-week discrepancy was found between peak occurrences of ILI predicted by Watson and CDC reported data. Additionally, we developed a scoring method for ILI prediction from a Twitter post using a simple formula with the ability to predict ILI two weeks ahead of ILI data as reported by CDC. The obtained results suggest that data found within social media can be used to supplement the traditional surveillance in epidemics of infectious diseases such as influenza or more recently COVID-19 with the help of intelligent computations