This paper presents research conducted to establish how information is shared across the personal social network in the sensitive context of a health crisis. We worked with parents of very sick babies who were cared for in a hospital's Neonatal Unit (NNU). Through a combination of interviews, a focus group and surveys, we developed a user model of the information that parents wanted to share, and how they adapted this information to individual recipients. We then developed a prototype software tool which created adaptive updates for members of the parents' social network. The updates contained summaries of large volumes of complex medical data about the baby, non-medical information about the parents, and practical information about the hospital. Updates were automatically adapted to individual members of parents' social networks, based on our user model. The tool was evaluated in a large NNU in the UK, with parents of babies who were currently being cared for in the Unit. We found that parents adapted the information that they shared about themselves and their babies based on the emotional proximity of their network members. They gave most detail to those who were emotionally closest to them, and least to those who were less close. Parents also adapted information content to the recipient's tendency to worry and empathize. Two adaptive strategies were deployed by parents, (i) benign deceit - not telling the whole truth - and (ii) promotion of empathetic members of the social network to a higher level of emotional proximity, so that they were given more information. We generated a number of directions for future work, and issues to consider around designing adaptive mediated communications systems for sensitive contexts. These include the potential to generalise our model to other medical contexts, and considerations to apply when deliberately designing deceit into adaptive systems.
Moncur, W., Masthoff, J., Reiter, E., Freer, Y., & Nguyen, H. (2014). Providing adaptive health updates across the personal social network. Human-Computer Interaction, 29(3), 256-309. https://doi.org/10.1080/07370024.2013.819218