Authors: Nisha Salunkhe, Neha Lavand, Parth Nagarkar, Divija Kadam
Abstract: Misinformation on social media has become a major challenge due to the rapid spread of false and misleading information. This research explores data science approaches for detecting misinformation using machine learning, natural language processing, and deep learning techniques. The proposed approach includes data collection, preprocessing, feature extraction, model training, and evaluation. Techniques such as TF-IDF, Logistic Regression, Support Vector Machine, Random Forest, LSTM, and BERT can be used to classify social media content. The study also examines challenges such as class imbalance, multilingual content, changing misinformation patterns, and model explainability. The research highlights how data science can support automated misinformation detection and assist users and factcheckers in identifying potentially misleading content.
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