Problem
Combination of increased use of social media platforms in modern society and prevailing negative climate of web space results in bullying on social media platforms and increased number of cases of depression.
Solution
Raising awareness on the problem and encouraging people to engage in creating a positive climate in digital space. The solution elements include enforcing the bullying prevention guidelines for social media platforms and introducing a system that allows several layers of access to a personal account (i.e., groups of friends, family, classmates, etc.). Additional solutions include removing the visible count of likes on publications, dislike buttons, adding quick and accessible options to report comments.
Opposing Viewpoints
Reducing some functions in the social media platforms could potentially limit advertising content on the platform. An increased number of reports on bullying could slow down support’s processing of more severe cases, such as violence and inappropriate or dangerous content.
Research
Craig, W., Boniel-Nissim, M., King, N., Walsh, S. D., Boer, M., Donnelly, P. D., Harel-Fisch, Y., Malinowska-Cieślik, Gaspar de Matos, M., Cosma, A., Van den Eijnden, R., Vieno, A., Elgar, F. J., Molcho, M., Bjereld, Y., & Pickett, W. (2020). Social media use and cyber-bullying: A cross-national analysis of young people in 42 Countries. Journal of Adolescent Health, 66(6), 100–108.
The article presents an analysis of cases of bullying on social media among young people. The research results show a tendency to cyber-perpetration in bullying on social media platforms among girls (Craig et al., 2020). The article emphasizes the risks that cyber-bullying, cyber-victimization, and cyber-perpetration present to the younger generation.
Dhungana Sainju, K., Mishra, N., Kuffour, A., & Young, L. (2021). Bullying discourse on Twitter: An examination of bully-related tweets using supervised machine learning. Computers in Human Behavior, 120, 1-11.
In the article, the authors applied machine learning and big data analysis principles to cases of bullying found on one of the popular social media platforms, Twitter. The research analyzes the individual reasons behind bullying on social media platforms and defines different types of bullying. The research results show that most bullying tweets have self-disclose character, meaning that the platform serves as a source of support and cathartic discussions (Dhungana Sainju et al., 2021).
Bozyiğita, A., Utkub, S., & Nasibov, E. (2021). Cyberbullying detection: Utilizing social media features. Expert Systems with Applications, 179, 1-12.
The article discusses current techniques that detect cyber-bullying on social media platforms and provides valuable insight into the technical aspect of the theme. The study focuses on explaining how different social media features could be used to effectively detect cyber-bullying and covers different trends and tendencies in bullying on social media platforms. The authors state that social media features contribute to the character of cyberbullying (Bozyiğita et al., 2021).
References
Bozyiğita, A., Utkub, S., & Nasibov, E. (2021). Cyberbullying detection: Utilizing social media features. Expert Systems with Applications, 179, 1-12.
Craig, W., Boniel-Nissim, M., King, N., Walsh, S. D., Boer, M., Donnelly, P. D., Harel-Fisch, Y., Malinowska-Cieślik, Gaspar de Matos, M., Cosma, A., Van den Eijnden, R., Vieno, A., Elgar, F. J., Molcho, M., Bjereld, Y., & Pickett, W. (2020). Social media use and cyber-bullying: A cross-national analysis of young people in 42 Countries. Journal of Adolescent Health, 66(6), 100–108. Web.
Dhungana Sainju, K., Mishra, N., Kuffour, A., & Young, L. (2021). Bullying discourse on Twitter: An examination of bully-related tweets using supervised machine learning. Computers in Human Behavior, 120, 1-11.