Cyber-Bullying Detection in Social Media Platform using Machine Learning

Jain, Vaibhav; Saxena, Ashendra Kumar; Athithan, Senthil; Jain, Abhishek; Jain, Arpit

doi:10.1109/smart52563.2021.9676194

Cited by 11 publications

(1 citation statement)

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“…While a multitude of techniques have attempted to work on the exactness of the cyberbullying-related task somewhat, we accept that they have not completely used precise data on past text-based information, which we call the setting in the context of this study [9]. The main classification of setting is an inner setting that incorporates any non-text-based data that can be separated from the dataset itself, e.g., the client's liked posts, adherents, or multimedia content connected to the social media post, e.g., pictures and recordings [10,11].…”

Section: Related Workmentioning

confidence: 99%

A New Model for a Secure Social Media Application

Riad

Elhoseny

2022

Applied Sciences

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The progress of data technology and wireless networks is generated by open online communication channels. Unfortunately, trolls are abusing the technology for executing cyberattacks and threats. An automated cybersecurity solution is vital for avoiding the threats and security issues from social media. This can be a requirement for tackling and considering cyberbullying in various aspects including prevention of such incidents and automated detection. This study introduces a novel Artificial Fish Swarm Algorithm with Weighted Extreme Learning Machine (AFSA-WELM) model for cybersecurity on social media. The proposed model is mostly intended to detect the existence of cyberbullying on social media. The proposed model starts by processing the dataset and making it ready for the next stages of the model. It then uses the TF-IDF vectorizer for word embedding. After that, it uses the WELM model for the identification and classification of cyberbullying. Finally, the optimal tunning parameters used in the WELM model are derived for the AFSA model. The experimental analysis has shown that the proposed model achieves maximum accuracy compared with existing algorithms. Moreover, our proposed model achieves maximum precision–recall performance with various datasets.

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Section: Related Workmentioning

confidence: 99%