Stigma associated with depression and mental illness is a widespread problem, often leading to negative health outcomes and discrimination for people with these conditions. Common stigma reduction interventions focus on two strategies: education (increasing basic understanding of the condition) and social contact (humanizing or ‘putting a face’ to the condition). These interventions have been explored in various formats, but little is known about the potential of leveraging online social media outlets to reduce stigma. We propose BlueFriends, an application that seeks to reduce stigma by displaying a shareable information visualization graphic aimed at increasing both education and social contact. This application will employ a predictive model of depression detection on Facebook. It will visualize the potential proportion of depression in a user’s online social network and display an interactive comparison of depression prevalence with other common prevalence levels in the US. BlueFriends expands current stigma reduction interventions by leveraging online social environments. By creating customized visualizations that users can share within their network, we hypothesize that Bluefriends will prompt collective stigma reduction and societal depression awareness.

Haimson, O.L., Ringland, K.E., Simpson, S., and Wolf, C.T. Using Depression Analytics to Reduce Stigma via Social Media: BlueFriends. 2014.