Epitweetr: Early warning of public health threats using Twitter data
Authors: Espinosa Laura , Wijermans Ariana , Orchard Francisco , Höhle Michael , Czernichow Thomas , Coletti Pietro , Hermans Lisa , Faes Christel , Kissling Esther , Mollet Thomas 
Published: March 2021

SummaryBackgroundECDC performs epidemic intelligence activities to systematically collate information from a variety of sources, including Twitter, to rapidly detect public health events. The lack of a freely available, customisable and automated early warning tool using Twitter data, prompted ECDC to develop epitweetr.The specific objectives are to assess the performance of the geolocation and signal detection algorithms used by epitweetr and to assess the performance of epitweetr in comparison with the manual monitoring of Twitter for early detection of public health threats.MethodsEpitweetr collects, geolocates and aggregates tweets to generate signals and email alerts. Firstly, we evaluated manually the tweet geolocation characteristics of 1,200 tweets, and assessed its accuracy in extracting the correct location and its performance in detecting tweets with available information on the tweet geolocation. Secondly, we evaluated signals generated by epitweetr between 19 October and 30 November 2020 and we calculated the positive predictive value (PPV). Then, we evaluated the sensitivity, specificity and timeliness of epitweetr in comparison with Twitter manual monitoring.FindingsThe epitweetr geolocation algorithm had an accuracy of 30.1% and 25.9% at national and subnational levels, respectively. General and specific PPV of the signal detection algorithm was 3.0% and 74.6%, respectively. Epitweetr and/or manual monitoring detected 570 signals and 454 events. Epitweetr had a sensitivity of 78.6% [75.2% - 82.0%] and PPV of 74.6% [70.5% - 78.6%]; and the manual monitoring had a sensitivity of 47.9% [43.8% - 52.0%] and PPV of 97.9% [95.8% - 99.9%]. The median validation time difference between sixteen common events detected by epitweetr and manual monitoring was −48.6 hours [(−102.8) - (−23.7) hours].InterpretationEpitweetr has shown to have sufficient performance as an early warning tool for public health threats using Twitter data. Having developed epitweetr as a free, open-source tool with several configurable settings and a strong automated component, it is expected to increase its usability and usefulness to public health experts.FundingNot applicableResearch in contextEvidence before this studyPrevious reviews have shown how social media, including Twitter, have been used for public health purposes. Most recent studies, in relation to the COVID-19 pandemic, have shown the added value of early warning tools based on Twitter and other social media platforms. They also noted the lack of an open-source tool for real-time monitoring and surveillance.Added value of this studyEpitweetr is a free, open-source and R-based early warning tool for automatic Twitter data monitoring that will support public health experts in rapidly detecting public health threats. The evaluation of epitweetr presented in this study shows the strengths of the tool which include having good performance, high degree of automation, being a near-real-time tool and being publicly available with various customisable settings. Furthermore, it shows which are the areas of improvement for the next versions of epitweetr.Implications of all the available evidenceThis tool can be further developed to include more automation and machine learning components to increase usability and information processing time by users.