Fake News Detection on Instagram through Feature Extraction and SVM based Analysis
Keywords:
Fake News Detection, Instagram, Support Vector Machine, Misinformation, Machine Learning, Data AnalysisAbstract
Fake news on social media can threaten people. It can affect their confidence level and decision-making. Few advanced techniques need to be utilized in this case of threats, which can detect the fake news. The main goal of this study is to create a strong framework for Instagram fake news detection using Support Vector Machine (SVM). By examining user-generated content on Instagram, this study aims to develop a novel method for effectively detecting fake news. On Instagram, deepfake videos manipulate photos, false captions, and fabricated comments that magnify false information are all used to produce and disseminate fake news. The study entails gathering publicly accessible datasets, such as user interactions and labeled news articles. These datasets are preprocessed using methods like feature extraction and text cleaning tokenization to highlight important information for model training. Metrics such as accuracy, precision recall, and F1-score are used to evaluate the performance of the SVM classifier, which is used for classification. Data analysis tools like Python and the Scikit-learn library are used to apply the machine learning model and assess its effectiveness. According to the study, fake news can be effectively and accurately identified by the SVM-based model, offering a workable solution to the problem of misleading information on Instagram. The results validate the feasibility of the proposed approach, thereby bolstering ongoing efforts to counteract fake news.
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