Twitter Data for Syndromic Surveillance: Insights and Methods

Monitoring public health, vital for disease control, often relies on traditional surveillance methods. However, emerging approaches like syndromic surveillance offer the potential for early detection and rapid response to outbreaks. This paper explores the viability of Twitter data for syndromic surveillance, aiming to assess its timeliness and accuracy in detecting disease trends. The study sheds light on the challenges and opportunities in utilizing social media platforms for public health monitoring through rigorous investigation into data collection, preprocessing, and feature extraction techniques. Additionally, it delves into the nuances of keyword selection and language dynamics on Twitter, underscoring the need for adaptive strategies in data collection. The paper concludes with avenues for future research, including developing automated keyword selection algorithms and integrating geolocation data to enhance surveillance capabilities.