No abstract

It was investigated whether different types of mental or developmental disorders (MDD) would be rated differently in terms of stereotypic perceptions and behavioral tendencies and whether these effects of stereotypes on behaviors would be mediated via emotional responses in line with the Stereotype Content Model (SCM). Furthermore, an experimental investigation sought to ascertain whether predictions about behavioral intentions of bystanders in a cyberbullying scenario towards a victim with MDD could be derived from the general behavioral tendencies as predicted by the SCM. Two-hundred-forty-eight undergraduate students (62% female) aged 18-35 (M = 22.5) were randomly allocated to one of five conditions (anxiety, depression, schizophrenia, Asperger's, or control). Stereotypes, emotional and behavioral responses, as well as bystander intentions were assessed. Results largely confirmed the general application of the SCM to MDD; however, this was not the case for bystander intentions in cyberbullying. Implications for the application of the SCM and methodological considerations are discussed.

There is some indication that discrimination as well as low levels of life satisfaction render young people at risk of cyberhate victimization. Adopting a socio-ecological perspective, this paper examines whether supportive family, peer and school environments may buffer against the effects of perceived discrimination and low life satisfaction on cyberhate victimization. Data from four countries ( N = 3396) of the EU Kids Online IV survey on children aged 11–17 (51% girls) revealed a positive association between perceived discrimination and cyberhate victimization, but this impact was moderated by supportive family and peer environments. A negative association between life satisfaction and cyberhate victimization was mitigated by peer support. However, no associations with the school context were found. The current study provides new insights on how social support on different levels of the social environment may buffer against potential risk factors for cyberhate victimization and can inform decision-makers towards intervention and prevention strategies.