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@article{167666,
        author = {G TEJASRI and B MURALI},
        title = {DETECTION OF CYBERBULLYING ON SOCIAL MEDIA BY USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {39-43},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167666},
        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 platorms 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%.. Researches on Cyberbullying Incidents show that 11.4% of 720 young peoples surveyed in the NCT DELHI were victims of cyberbullying in a 2018 survey by Child Right and You, an NGO in India, and almost half of them did not even mention it to their teachers, parents or guardians. From this project we hope to reduce the amount of cyberattacks across the globe. This paper mainly works on algorithms proposed by famous researchers and educators},
        keywords = {Cyberbullying, Internet Safety, Suicide Prevention, Depression Prevention, Social Media Regulation, Hate Speech Detection},
        month = {September},
        }