Detection of Cyberbullying on Social Media Using Machine learning

IEEE BASE PAPER ABSTRACT:

Cyberbullying is a major problem encountered on internet that affects teenagers and also adults. It has lead to mishappenings like suicide and depression. Regulation of content on Social media platforms has become a growing need. The following study uses data from two different forms of cyberbullying, hate speech tweets from Twittter and comments based on personal attacks from Wikipedia forums to build a model based on detection of Cyberbullying in text data using Natural Language Processing and Machine learning. Three methods for Feature extraction and four classifiers are studied to outline the best approach. For Tweet data the model provides accuracies above 90% and for Wikipedia data it gives accuracies above 80%.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Support Vector Machine, Random Forest Classifier.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Varun Jain, Vishant Kumar, Vivek Pal, Dinesh Kumar Vishwakarma, “Detection of Cyberbullying on Social Media Using Machine learning”, IEEE Conference, 2021.

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Detection of Cyberbullying on Social Media Using Machine learning