Kaitlin Light Costello
School of Communication & Information Rutgers, the State University of New Jersey, United States of America

Monday, November 9, 1:30pm

This paper describes a longitudinal analysis of online social networks designed for patients diagnosed with chronic kidney disease. Observational analysis of 20 indicators in three domains – practices for auditing and moderating the quality of content provided by users, accessibility of privacy policies, and data sharing policies and member control over data sharing – was conducted on 10 sites in 2013 and again on 12 sites in 2015, with 7 of the same sites included in both samples. The sites were identified using Google. Indicators for each domain were scored dichotomously. These scores were compared among sites in order to analyze their general practices and policies. Total composite scores were also analyzed to determine whether individual sites had significantly different practices and policies in comparison with the group. Finally, scores for each domain were compared across years in order to assess whether practices and policies had changed over time.

Differences in site practices and policies between 2013 and 2015 were not significant, although there is much room for improvement in all domains. Quality was variable across all sites, with gaps in medical disclaimers, a lack of external review of privacy policies and data safety audits, and missing information about internal quality control in the form of moderators. Although most sites employ moderators, their credentials are not often reported. Privacy policies are inaccessible across the board, with none readable at below a twelfth-grade level. Data safety practices are also problematic, with most sites sharing user data with third parties.

The quality and safety of social networks for CKD is variable, and improvement is feasible. Suggested improvements include auditing privacy policies and data safety practices, making information about moderators easily available, and third-party audits of information posted by users to ensure the removal of misinformation.