Social media platforms have become ubiquitous and allow users to share information in real-time. Our study uses data analytics as an approach to explore non-communicable diseases on social media platforms and to identify trends and patterns of related disease symptoms. Exploring epidemiological patterns of non-communicable diseases is vital given that they have become prevalent in low income communities, accounting for about 38 million deaths worldwide.

We collected data related to multiple disease conditions from the Twitter microblogging platform and zoomed into symptoms related to heart diseases. As part of our analyses, we focused on the mechanism and trends of disease occurrences.

Our results show that specific symptoms may be attributed to multiple disease conditions and it is viable to identify trends and patterns of their occurrences using a structured analytics approach. This can then act as a supplementary tool to support epidemiological initiatives that monitor non-communicable diseases. Based on the study’s results we identify that non-communicable disease surveillance approach using social media analytics could support the design of effective health intervention strategies.