A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media
In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.
keywords: Social Media, text mining, depression, public health surveillance, stream processing, real-time processing