Historically the disease outbreaks were detected based on trends observed in the official reports collected at various geographic levels as part of the pre-established surveillance programs. The major drawback of this approach is producing outbreak alerts in timely fashion. Advances in technology and rapid adoption of information sharing platforms such as social media platforms provide new data sources and unique opportunities for researchers to study disease outbreaks. Digital disease surveillance involves monitoring various digital information sources for early warning, detection, rapid response, and management phases. Unlike manual systems, which relies on traditional disease surveillance program reports to monitor and predict early outbreaks, the current automated digital disease surveillance systems exploit mainly publicly available information on internet such as news, social media and search engines. The objective of this workshop emphasizes the application of the latest advances in advanced data mining algorithmic methods such as deep learning and online learning approach on social media data to detect early signals for an outbreak using social media.