Citation

Ambareen K, Meenakshi Sundaram S. SN Comput. Sci. 2023; 4(6).

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s42979-023-02301-2

PMID

unavailable

Abstract

Recently, cyberbullying has become one of the most important topics on social media. Online social media users have recognised this as a severe problem, and in recent years, effective detection models have been developed. This has taken on substantial importance. The numerous forms of cyberbullying on social media are highlighted by this poll. Currently, research is being done to identify cyberbullying using AI approaches. We talk about various machine learning and natural language processing (NLP) methods that are used to identify cyberbullying. Additionally, the difficulties and potential directions for future research in the area of AI detection of cyberbullying have been discussed. Attacks on victims of cyberbullying have surged by 40% in 2020's pandemic season. 20% of the increase in juvenile suicides is attributable to cyberbullying. Attacks involving cyberbullying are expected to reach an all-time high in 2025, according to 60% of experts. 38% of respondents report daily exposure to cyberbullying on social media platforms. Even though many people are aware of cyberattacks, cyberbullying has begun to rise alarmingly. By keeping track of the signs of cyberbullying before it occurs, internet service providers can develop more precise classifications for the behaviour to prevent it. Large data sets can also be processed using deep learning techniques. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

Language: en

Keywords

Algorithms; Cyberbullying; Social media; Twitter; Machine learning techniques; Cybercrime detection