Social support in cyberspace: A content analysis of communication within a Huntington’s Disease online support group

Objective: Huntington’s Disease (HD) is an inherited disorder, characterised by a progressive degeneration of the brain. Due to the nature of the symptoms, the genetic element of the disease and the fact that there is no cure, HD patients and those in their support network often experience considerable stress and anxiety. With an expansion in Internet access, individuals affected by HD have new opportunities for information retrieval and social support. The aim of this study is to examine the provision of social support in messages posted to a HD online support group bulletin board. Methods: In total, 1313 messages were content analysed using a modified version of the Social Support Behaviour Code developed by Cutrona & Suhr (1992). Results: The analysis indicates that group members most frequently offered informational (56.2%) and emotional support (51.9%) followed by network support (48.4%) with esteem support (21.7%) and tangible assistance (9.8%) least frequently offered. Conclusion: This study suggests that exchanging informational and emotional support represents a key function of this online group. Practice implications: Online support groups provide a unique opportunity for health professionals to learn about the experiences and views of individuals affected by HD and explore where and why gaps may exist between evidence-based medicine and consumer behaviour and expectations.

A Recommender System based on the Immune Network

Abstract-The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen - antibody interaction for matching and antibody - antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

A Recommender System based on the Immune Network

Abstract-The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen - antibody interaction for matching and antibody - antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.