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@article{187645,
        author = {Aditi Uday Sandbhor and Vaishnavi Channappa Burhanpure and Shraddha Ajaykumar Shahi},
        title = {Hate Speech and Fake News Detection on Social Media Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {7124-7127},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187645},
        abstract = {As social media platforms have grown rapidly, the proliferation of hate speech, disinformation, and fake news has also increased. This type of content can sway public opinion, incite social strife, and diminish our trust in online communications. This research seeks to use algorithms in machine learning to identify hate speech and fake news using datasets available to the public. This investigation employs Natural Language Processing (NLP), Term Frequency-Inverse Document Frequency-TFIDF feature extraction, and implementations of logistic regression, Support Vector Machine (SVM), and random forests as classifiers. Our experimental results show SVM with a linear kernel lead to the highest accuracy for both the hate speech and fake news datasets and demonstrate the viability of linear models for text classification tasks. The findings will support online safety and the automation of moderation systems in digital communication platforms.},
        keywords = {Hate Speech, Fake News, Machine Learning, NLP, TF-IDF, Social Media, SVM, Text Classification},
        month = {November},
        }