(De)Stigmatizing Depression on Social Media: The Role of Responsibility Frames

Abstract

Responsibility frames on social media could shape recipients’ responses toward people with depression, which is crucial for the public (de)stigmatization of the mental disorder. Thus, the present study examines the effects of different responsibility frames (individual, social, combination) in Instagram-posts about depression on respondents’ related attributions as well as their emotional and behavioral reactions toward people suffering from the illness. Our online-experiment (N = 1,015) revealed that frames emphasizing the responsibility of one’s social network (e.g. family, friends and professionals) for depression, i.e. social frames, strengthened participants’ attributions to the social network, i.e. social attributions, most effectively. Individual frames, however, primarily intensified individual attributions to those affected by depression. Contrary to previous findings, a combination frame did not prove to increase recipients’ social attributions more than a one-sided social frame. For emotional and behavioral responses, we did not find any effects of responsibility frames compared to the control group–possibly due to buffering effects of the narrative structure of the Instagram posts.

Data availability statement

The data we used has not been published so far. If needed, our data can be made available.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical Statement

We confirm that our study meets ethical guidelines to the requirements of the German Psychological Society. Our research obtained approval from the ethic committee of the University of Erfurt (approval number: 20210107).

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10810730.2023.2266702.

Notes

¹ An a priori power analysis yielded for experimental designs with four groups of equal size a sample size of N = 824 persons in order to prove a small effect size (α = .05, f = .10, power = 0.80).

² Mediation analysis was conducted with Hayes Macro for SPSS using model 82 (Hayes, 2018). Following Hayes and Preacher (2014) we specified that our predictor is multicategorical. Using indicator coding we estimated the relative indirect effects of the responsibility frames compared to the control condition. Bootstrapping with 5,000 iterations was used to compute 95% confidence intervals (CI).

Additional information

Funding

The work was supported by the German Research Foundation (DFG) [4048819799].