| Popis |
Adolescents are increasingly exposed to cyberhate—online hate speech and biased content (Kardefelt Winther et al., 2023; Machackova et al., 2020), raising concerns about its long-term psychological and behavioural consequences. While existing research highlights the harmful effects (Jaron Bedrosova et al., 2024), the processes through which frequent exposure influences psychological and behavioural outcomes remain underexplored. To address this gap, we adopt desensitisation and social learning frameworks (Allen et al., 2018; Anderson & Bushman, 2002; Bandura, 1978) to shed more light on these mechanisms. Prior research with adults links short-term cyberhate exposure to desensitisation (Soral et al., 2018, 2023), but long-term effects and effects on adolescents are less understood. Yet adolescents’ identity and intergroup attitudes are developing (Cortese, 2005) and may be shaped by frequent exposure to cyberhate’s biased messages. Testing the desensitisation assumption, we investigate whether frequent cyberhate exposure increases adolescents’ moral disengagement (i.e., minimising consequences of cyberaggressive incidents). Further, youth cyberhate exposure has been cross-sectionally linked to perpetration (Wachs & Wright, 2018; Wachs et al., 2021). Adopting the social learning theory, we investigate whether frequent exposure fosters learned cyberhate behaviours in the form of cyberhate aggression. We also explore whether moral disengagement mediates this association. We will use longitudinal online survey data from 3,087 Czech adolescents (ages 11-16, M=13.47, SD=1.74; 50.1% boys) collected over four waves, six months apart, in 2021-2022. We used quota sampling to ensure (1) that included households represent Czech households with children in terms of SES, region, and municipality size, and (2) balanced age and gender groups. We measured cyberhate exposure and perpetration using two single-item measures and moral disengagement using a four-item scale (adapted from Garland et al., 2016). We will test within-person longitudinal effects between these variables using the random intercept cross-lagged panel model (Hamaker et al., 2015).
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