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Stay Safe and Strong: Characteristics, Roles and Emotions of Student-Produced Comics Related to Cyberbullying.

Consuelo MameliLaura MenabòAntonella BrighiDamiano MeninCatherine CulbertJayne HamiltonHerbert ScheithauerPeter K SmithTrijntje VöllinkRoy A WillemsNoel PurdyAnnalisa Guarini
Published in: International journal of environmental research and public health (2022)
The present study aimed at giving voice to students from disadvantaged socio-economic backgrounds using a co-participatory approach. Participants were 59 adolescents (52.5% males) aged between 14 and 16 from five European countries who created ten comics to illustrate cyberbullying for a broader audience of peers. We analyzed texts and images according to four primary themes: cyberbullying episodes (types, platforms, co-occurrence with bullying), coping strategies, characters (roles, gender, and group membership), and emotions. The content analysis showed that online denigration on social media platforms was widely represented and that cyberbullying co-existed with bullying. Social strategies were frequently combined with passive and confrontational coping, up to suicide. All roles (cyberbully, cybervictim, bystander, reinforcer, defender) were portrayed among the 154 characters identified, even if victims and defenders appeared in the vignettes more often. Males, females, peers, and adults were represented in all roles. Among the 87 emotions detected, sadness was the most frequently expressed, followed by joy, surprise, anger, and fear. Emotions, mainly represented by drawings or drawings with text, were most often represented in association with cybervictims. The results are discussed in terms of their methodological and practical implications, as they emphasize the importance of valorizing young peoples' voices in research and interventions against cyberbullying.
Keyphrases
  • social media
  • health information
  • high school
  • physical activity
  • depressive symptoms
  • young adults
  • mental health
  • healthcare
  • social support
  • deep learning
  • convolutional neural network
  • machine learning
  • medical education