A Machine Learning Approach to Identify Cyberbullying
Author(s):
Chetan K. Shisode, Girish C. Patil, Chetan S. Patil, Mahesh S. Patil, Kalpesh M. Patil
Keywords:
Cyberbullying Detection, Social Media, Machine Learning Algorithm.
Abstract
In recent years, there has been a fast expansion of information or knowledge which has been driven by the internet. As a result of the rise of online social media, new people join these online platforms, significantly boosting their use while also increasing the incidents of hate speech. There are some existing systems which detect hate speeches on social media platforms. The proposed system aims to detect cyberbullying comments using machine learning techniques. Cyberbullying can take several forms that include threats, hate mails, toxic words, etc. Prevention of cyberbullying has become mandatory. The project focuses on leveraging machine learning algorithms to effectively detect and prevent cyberbullying, a pervasive issue in online spaces. Through the implementation of advanced computational techniques, the system demonstrates promising outcomes in identifying and addressing in stances of online harassment. By combining sentiment analysis and innovative algorithms, the proposed system aims to create a safer digital environment by proactively identifying and mitigating cyberbullying instances, thus fostering a positive online experience for users.
Article Details
Unique Paper ID: 163424

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 2434 - 2437
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