Who tweets what about personalised medicine? Promises and concerns from Twitter discussions in Denmark

Abstract

Digital health data are seen as valuable resources for the development of better and more efficient treatments, for instance through personalised medicine. However, health data are information about individuals who hold opinions and can challenge how data about them are used. Therefore it is important to understand public discussions around reuse of digital health data. Social media have been heralded as enabling new forms of public engagement and as a place to study social issues. In this paper, we study a public debate on Twitter about personalised medicine. We explore who participates in discussions about personalised medicine on Twitter and what they tweet about. Based on user-generated biographies we categorise users as having a ‘Professional interest in personalised medicine’ or as ‘Private’ users. We describe how users within the field tweet about the promises of personalised medicine, while users unaffiliated with the field tweet about the concrete realisation of these ambitions in the form of a new infrastructure and express concerns about the conditions for the implementation. Our study serves to remind people interested in public opinion that Twitter is a platform used for multiple purposes by different actors and not simply a bottom-up democratic forum. This study contributes with insights relevant to policymakers wishing to expand infrastructures for reuse of health data. First, by providing insights into what is discussed about health data reuse. Second, by exploring how Twitter can be used as a platform to study public discussions about reuse of health data.

Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox

“…Twitter is the most used social media platform amongst journalists [5] and ranks amongst the most popular social media platforms on a global scale [6]. Twitter has been highly popular amongst data scientists and computer science researchers for studying, analyzing, modeling, and interpreting social media communications related to various topics, such as ChatGPT [7], the Russia-Ukraine war [8], cryptocurrency markets [9], virtual assistants [10], mental health [11], loneliness in the elderly [12], housing needs of low-income families [13], animal welfare [14], climate change [15], cognitive impairment [16], the electronics industry [17], agriculture [18], race and ethnicity [19], fake news [20], abortion [21], religion [22], fall detection [23,24], gender identity [25], elections [26], politics [27], food insufficiency [28], pregnancy [29], drug safety [30], indoor localization [31], gambling [32], education systems [33], exoskeletons [34], personalized medicine [35], natural disasters [36], crimes [37], democracy [38], and transportation [39], just to name a few. In addition to the above, Twitter data mining and analysis has also attracted the attention of healthcare researchers, epidemiologists, and medical practitioners, as is evident from several works that focused on the mining and analysis of tweets related to pandemics, epidemics, viruses, and diseases such as Ebola [40], E-Coli [...…”