Abstract
Online health support communities play an important role in the exchange of social support and the improvement of health. There is a dearth of theoretical frameworks dedicated to a better understanding of self-disclosure and supporting exchange behaviour in the online health support community literature based on a data-driven research approach. Using a critical review of the literature, this chapter seeks to define the relationship between user support exchange behaviour and self-disclosure intention by modifying the theory of reasoned action (TRA) whilst introducing a new concept called ‘altruistic user role', which describes a user's proclivity to provide or seek social support. A combination of methods, including manual coding and machine learning algorithms, was employed. To help improve the design of online health support communities, this article discusses the implications of the proposed modified TRA.
TopIntroduction
People are seeking and sharing health information online in greater numbers as information and communication technologies (ICTs) advance (Oh, & Syn, 2015). Seventy-two per cent of adult internet users in the United States have reported looking for medical information online (Rideout, & Fox, 2018). Online health support communities (OHSCs) have risen to prominence as a valuable resource for doctors, patients, and caregivers (Liu, Liu, & Guo, 2020). The literature shows that online health support communities play a critical role in creating a virtual space that is accessible to people from all over the world for those dealing with potentially fatal diseases (Abiola, Udofia, & Zakari, 2013; Huang, ChengalurSmith, & Pinsonneault, 2019). This is especially important for chronic diseases like diabetes, where patients expect not only to receive ongoing medical treatment but also to receive support and companionship from others who have had similar experiences. In exchange for personalised suggestions and peer support, one user participation mechanism commonly seen in online health support communities is the generation of a large amount of personal information and emotional feelings (Fernandes, & Costa, 2021). According to previous research, online health support community users are willing to share their personal information publicly to take advantage of the convenience of online services (Atanasova, Kamin, & Petrič, 2018; Cavusoglu Phan, Cavusoglu, & Airoldi, 2016; Jozani, Ayaburi, Ko, & Choo, 2020). Online health support community users can gain medical knowledge, emotional comfort, and strengthen both online and offline social connections through active engagement (Wang, Zhao, & Street, 2017). As a result, user-generated content, particularly those involving self-disclosure, are the fundamental building blocks that distinguish online health support communities and contribute to both the provision and the seeking of social support.
Although online health support community users’ information disclosure “may meet their basic needs for obtaining social support and forming social connections, when they give up some degree of privacy and personal control,” they risk exposing their personal data (Liu, Miltgen, & Xia, 2022). Meanwhile, disclosed personal information leads to easily retrievable digital traces that can be collected by a variety of parties, resulting in unexpected privacy intrusions (Jain, Sahoo, & Kaubiyal, 2021) such as malicious attacks (e.g., phishing), illegal interests (e.g., doxing), and crimes (e.g., burglary, racketeering, and robbery). According to Walters (2017), most adult internet users are concerned about their personal information being disclosed on the internet, which can be used to identify a user’s political inclination, purchasing habits, lifestyles, and so on (Wu, 2019).
As a result, digital footprints can be consolidated to profile a person efficiently and precisely, revealing more information than ever before. Health data, as a type of personal information, is extremely sensitive and valuable, if not the most valuable, to not only the individuals who possess it but also to companies and governments, particularly when the contents are expressed through personal narratives (Ma, Zuo, M., & Liu, 2021). Users’ lack of awareness of privacy management during information exchange in online health support communities may result in the disclosure of personal characteristics such as identity, medical records, test results, and insurance details, to name a few. Furthermore, sensitive information like this can accumulate over time, resulting in unintended consequences and biases against users, even if the disclosure is intended for seeking or providing support with fellow online health support community users. For example, a patient’s therapeutic trajectory can be determined by analysing many of their online posts over time (Abiola et al., 2013; Taylor, & Pagliari, 2018). Similarly, patients in online health support communities face a dilemma of self-disclosure: while it is associated with negative outcomes, it is unavoidable in obtaining social support from other online health support community users.
Key Terms in this Chapter
Social Support Exchange: This is the exchange, or give-and-take, of psychological and/or material resources between and among members of a social network of friends, families, and others to assist individual members affected by a health challenge, such as stress, diabetes, or cancer, in coping with the challenges and improving their well-being.
Health Communication: This refers to the multifaceted and multidisciplinary approach to reach different audiences and share health-related information with the goal of influencing, engaging, and supporting individuals, communities, health professionals, special groups, policymakers and the public to champion, introduce, adopt, or sustain a behaviour, practice, or policy that will ultimately improve health outcomes.
Altruistic User Role: The dynamics of the various roles a user plays while providing and/or seeking social support online are referred to as the altruistic user role. Individual users’ intentional contributions in online health support communities may pass through various levels, influenced by mechanisms such as reciprocity, peer recognition, and selfimage. During various stages of involvement, some users may play prominent social roles in seeking and providing social support. Individuals are expected to behave differently and play different roles in online communities based on their two basic interests, one for themselves and the other for others. Self-centred users, in particular, are preoccupied with their own health issues, whereas altruistic users are more concerned with the needs of others.
Data-Driven (Research) Approach: Simply put, this term refers to the exploratory approach that analyses data to extract scientifically interesting insights (such as patterns, e.g.,) by applying analytical techniques and modes of reasoning.
Identity Information: This refers to narrative content about a user’s identity, such as name, gender, resident location, emails, photos, affiliations, and other narrative content about a user’s identity are examples of identity information. Although this genre is rarely directly related to users' knowledge or physical well-being, it does impose high privacy costs that prevent users from disclosing such information.
Online Self-Disclosure (Intention): In a nutshell, this term refers to the voluntary disclosure of personal information to others with whom one is in an interpersonal relationship in certain online environments or spaces. It also refers to individuals in interpersonal relationships voluntarily disclosing vital personal information about themselves to others in online, or virtual environments/spaces such as social media platforms, ecommerce, and geographical location-based services such as GPS.
Online Social Support: Online social support refers to a resource exchange between individuals in online or virtual environments to improve the receiver's wellbeing. Community psychologists, for example, have identified various types of social support based on the nature of exchanged resources, such as informational, emotional, companionship, and instrumental support. Informational and emotional support are the most common types of social support, so they are the focus of research into social support in online communities. In contrast to online information support, instrumental support, which refers to the assistance received physically or tangibly such as calling for an ambulance or receiving medicine delivery, is usually limited by geographical proximity.
Machine Learning Algorithms: Pre-programmed algorithms receive and analyse input data to predict output values that are within an acceptable range. These algorithms learn and optimise their operations as new data is added, improving performance, and developing intelligence over time. Algorithms are a set of rules that a computer follows to accomplish a specific goal procedure for solving.
Personal Health Information: Personal health information refers to narrative content about a user's identity that is related to their health care status and histories, such as symptoms, treatments, outcomes, diagnosis time, treatment progress, and regimen.
Online Health Support Communities: These are online social platforms or networks where members assist one another in dealing with health-related issues. Caregivers and community members frequently share information with fellow patients and their families to educate them about illnesses, seek and provide social support, and even form networks with others in similar situations.