“Technology enabled Health” - Insights from twitter analytics with a socio-technical perspective

Technology had been used in health domain for various purposes such as for storing electronic health records; monitoring; education; communication; and for behavioural tracking. The evident benefits have triggered a huge amount of discussions surrounding health technology in the web 3.0 space and users around the globe are sharing their experiences and perspective on social media platforms. Social media had been used for creating awareness, sharing information and providing emotional support to public in different diseases. This study focuses on exploring the health technology related discussions in Twitter. For this study around 105,489 tweets were collected from Twitter by 15,587 unique users. These tweets were analysed through social media analytics approaches (i.e. CUP framework). The study presents the top technologies in health domain through hashtag analysis and top diseases (acute, chronic, communicable and non-communicable) through word analysis and their association through co-occurrence of words within the tweets. The association depicts technology had been used in treating, identifying and heeling of the various diseases. The discussion on social media is skewed towards computing algorithms. The acute and chronic diseases were discussed on social media, and our study indicates that statistically, there is no difference in the discussion of acute and chronic diseases. The communicable and non-communicable diseases are also discussed on social media, and our study indicates no statistically difference in the discussion of communicable and non-communicable diseases which signifies users are referring to Twitter for discussing various type of diseases irrespective of acute, chronic, communicable and non-communicable diseases. Future researchers can use the study as the evidence of extracting insights related to socio-technical perspective from Twitter data. The literature contains lot of evidences where technology had been useful in health domain, but the bigger picture of how the various technologies are being related to health domain is missing, therefore this study tries to contribute to this area by mining tweets.