Author(s):
1. Saba Yousha:
Department of Computer Information & Engineering, Mehran University of Engineering and Technology Jamshoro SINDH, Pakistan
2. Sajjad Ali Memon:
Department of Telecommunication Engineering, Mehran University of Engineering and Technology. Jamshoro SINDH, Pakistan
3. Shahnawaz Talpur:
Department of Computer System Engineering, Mehran University of Engineering and Technology, Jamshoro, SINDH, Pakistan
Abstract:
The widpread dominance of the Internet and Electronic Media has made Social Media platforms a primary mode of communication. Unfortunately, these platforms have also become breeding grounds for harmful behavior, notably "Cyber Bullying," which involves using technology to inflict disrespect and harm on others. Despite various efforts by researchers to address this issue, the detection of such behavior remains crucial in combating this menace. This study aims to emphasize an effective approach for detecting cyberbullying on Social Media platforms. The findings indicate that the SVM (Support Vector Machine) classifier outperforms other classifiers in this context. We acquired tweet data from Twitter and used significant machine learning techniques to classify and forecast whether tweets are "offensive" or "non-offensive" and after that, using the Support Vector Machine's Algorithm, a machine learning-model is prepared to detect Cyber Bullying on Social Media Platform. This research provide promising results to use ML techniques for detection of Cyber Bullying.
Page(s):
760-772
DOI:
DOI not available
Published:
Journal: International Journal of Innovations in Science & Technology, Volume: 5, Issue: 4, Year: 2023
Keywords:
machine learning
,
detection
,
Cyberbullying
,
support vector machine SVM
,
Social Media Platforms
References:
References are not available for this document.
Citations
Citations are not available for this document.