For over 2 years, Symplur has collected health conversations on Twitter. From a humble start, our infrastructure has grown and matured to scale with the tremendous growth of healthcare social media. We’re now approaching 150 million health tweets in our database which we analyze and categorize by thousands of variables. And similarly with other repositories of big data, our greatest challenge is to figure out how to present insights from such a vast dataset in a meaningful way. The answer is often found in visualizations.
We plan to start a small series of short posts displaying some of this data in a visual way.
What you see in the video below is a 22-month timeline of about 2,000 different health communities and topics each visualized as a bubble. In total, about 100 million healthcare tweets are represented in this visual. The green dots symbolize patient-centric topics, while pink encompasses more professional/provider topics. Larger bubbles signify larger volume of conversations within that community. The data is visualized dynamically over the this 22-month time period starting September 2010.
Why the growth of Patient Communities on Twitter?
What we discovered was somewhat of a surprise. From the start, many considered Twitter as a kind of virtual water cooler, mostly used by healthcare professionals. It’s been thought that the need for privacy would push patients to more closed platforms. Twitter as you know, is totally public and should not be considered private.
However, from what you can clearly observe, the green bubbles have grown in numbers and significance quite dramatically in past months. This indicates a strong growth of conversations in existing patient communities on Twitter and also a growth of new patient communities on Twitter within our dataset.
Why this growth? Why would patients choose to utilize a communication platform without privacy? We will continue this visual journey trying to give possible answers to that question.
Update: The second post in this series is now published – The Dynamics of a Twitter Patient Community – Network Centrality Analysis