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@article{156585,
        author = {M.S.Jothi and M.Goudhaman},
        title = {Detecting and Preventing Cyber Bullying Comments on Social Media Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {4},
        pages = {301-308},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156585},
        abstract = {Everyone has the right to express themselves freely. However, this right is abused under the pretext of freedom of expression to discriminate verbally or physically, or to hurt people. Such intolerance is called hate speech. Hate speech is defined as language that expresses hostility towards an individual or group based on characteristics such as race, religion, ethnicity, gender, national origin, disability, or sexual orientation. It can take the form of statements, sentences, actions, or depictions that single out a person for belonging to a particular group. Both offline and online, hate speech has grown in importance in recent years. Hate content is increasingly proliferated on social media and other online platforms, ultimately leading to hate crimes. Humanity has greatly benefited from the use and information sharing of social media platforms. Nevertheless, this caused many problems, including the spread of hate speech. To address this growing problem on social media platforms, recent research has combined various machine learning and deep-his learning approaches with text-his mining techniques to automatically generate hate speech on real-time datasets. detected at Therefore, the purpose of this research is to review different hate speech detection algorithms and predict the best ones for social media datasets. Additionally, hate speech detection for real-time social settings is now enabled via mobile phone notifications.},
        keywords = {Social Media, Hate Speech, Deep learning, Text mining.},
        month = {},
        }