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Top1. Introduction
Decision making in the use of technology is important, with concerns over users being truly informed about the choices they make online (Williams, Burnap, Sloan, Jessop, & Lepps, 2017). Many social networking sites exist where individuals create public profiles within a service, connecting with other users (Boyd, 2007; Jeong & Kim, 2017; Li, Cheng, & Teng, 2020; Ortiz, Chih, & Tsai, 2018; Rathore, Sharma, Loia, Jeong, & Park, 2017). A growing number of users leverage online fora in an attempt to finding information relevant to their healthcare needs (Choi et al., 2017). This has led to the development and proliferation of Health Social Networks (HSNs).
HSNs are online services where people connect and share relevant health data (Li, 2013). HSNs offer users emotional support, Q&A with physicians, quantified self-tracking and/or access to clinical trials (Choi et al., 2017; O'Leary, Coulson, Perez-Vallejos, & McAuley, 2020). The key value for users is their ability to connect with others with similar health situations (Choi et al., 2017; Meng, 2016; Swan, 2009). When individuals share personal health information (PHI) online, they contribute to ‘big data sets’ that could potentially be used for medical research or by other third parties (Lee, Park, Chang, & Ko, 2019; Leon-Sanz, 2019; Murdoch & Detsky, 2013). Some HSNs sell anonymized PHI data to pharmaceutical companies, universities, and research labs (Bouraga, Jureta, & Faulkner, 2019; Kotsilieris, Pavlaki, Christopoulou, & Anagnostopoulos, 2017; Swan, 2009).
Existing research recognises the affordances of ‘big data’ in the health domain, extolling big data as an opportunity to leverage patient and practitioner data as a means of improving the quality and efficiency of healthcare systems (Horehájová & Marasová, 2020; McAfee, Brynjolfsson, & Davenport, 2012; Milenkovic, Vukmirovic, & Milenkovic, 2019). Big data has the potential to improve problem solving by providing greater insight into complex issues (Madden, Gilman, Levy, & Marwick, 2017). Given the existence of these big data sets the evolution of big data analytics is inevitable, bringing with it several challenges including the need to establish robust privacy and security standards and governance to protect patients and their PHI (Price, 2020; Price & Cohen, 2019; Raghupathi & Raghupathi, 2014; Sharma, Singh, & Rehman, 2020). Madden, Gilman, Levy & Marwick, (2017) remind us that big data holds the risk of information misuse, a “black box society” (Pasquale, 2015), a “transparency paradox” (Richards & King, 2013) and a lack of “algorithmic accountability” (Rosenblat, Kneese, & Boyd, 2014) where the individual is oblivious as to how their data is being manipulated (Madden et al., 2017).