A dual-process approach to stigma reduction using online, user-generated narratives in social media messages

Mental illness is a widespread public health concern. Stigma is a known barrier to recovery, and individuals often avoid seeking treatment because of it. The purpose of my research was to understand how individuals process peer-created, mental illness messages on social media, and to what extent these messages reduce stigma. I conducted two experiments based on the Elaboration Likelihood Model (ELM) to examine attitudes related to negative beliefs about mental illness and preferred social distance from mentally ill individuals.

Argument quality and amount of elaboration influenced empathetic responses to a message. Empathy was directly associated with a decrease in stigmatized beliefs about mental illness. Individuals who perceived that the message sharer was a close, trusted friend were more likely to indicate that the original message creator was more credible. Original message creators who disclosed having a mental illness were also perceived as more credible than creators who did not disclose having a mental illness. In addition, participants who perceived that the message sharer positively endorsed the message had less stigmatized beliefs about mental illness than participants who perceived negative endorsements.

Results of this project suggest that traditional ELM variables, such as elaboration and argument quality, influence the processing and outcomes of viewing social media messages about mental illness. Several new media characteristics, such as who shares the message online and comments they attach to the message, also influence how users think about the message and influence processing outcomes.