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Abstract
Cyberbullying is a growing problem across social media platforms, inflicting short and long-lasting effects on victims. To mitigate this problem, research has looked into building automated systems, powered by machine learning, to detect cyberbullying incidents, or the involved actors like victims and perpetrators. In the past, systematic reviews have examined the approaches within this growing body of work, but with a focus on the computational aspects of the technical innovation, feature engineering, or performance optimization, without centering on the roles, beliefs, desires, or expectations of humans. This study analyzed few papers based on a three-prong human-centeredness algorithm design framework – spanning theoretical, participatory, and speculative design. It was found that the past literature fell short of incorporating human-centeredness across multiple aspects, ranging from defining cyberbullying, establishing the ground truth in data annotation, evaluating the performance of the detection models, to speculating the usage and users of the models, including potential harms and negative consequences. Given the sensitivities of the cyberbullying experience and the deep ramifications cyberbullying incidents bear on the involved actors, takeaways on how incorporating human-centeredness in future research can aid with developing detection systems that are more practical, useful, and tuned to the diverse needs and contexts of the stakeholders were discussed. Key Words: Cyberbullying detection, human-centered machine learning, Human-centered computing, literature review, social media