Deteksi otomatis cyberbullying di media sosial: integrasi teknologi dan tantangan sosial remaja
Abstract
The phenomenon of cyberbullying is increasingly prevalent in the digital era, especially among teenagers who actively use social media. Conventional methods of moderating content are considered ineffective in addressing this problem. Therefore, automatic detection technology based on machine learning and natural language processing (NLP) was developed to recognize speech containing bullying actions. This article is a literature review that discusses the effectiveness of various computational methods in detecting cyberbullying, with a focus on language characteristics, classification models, and annotation techniques. This study also mentions the importance of considering the roles of perpetrators, victims, and witnesses in improving detection accuracy, as expressed by Van Hee et al. (2018). The results of the study indicate that automatic detection can be a tool in proactively moderating content, as long as it is implemented with ethical considerations and balanced with digital education efforts. The main contribution of this study is to develop a cyberbullying detection strategy that can form the basis for developing an intelligent moderation system and to prevent cases of online bullying among teenagers.
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References
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