Comparative Analysis Of Classification Algorithms For Social Media Misinformation
Abstract- Misinformation dissemination on social media platforms has seen a surge in the last few years, leading to severe consequences for individuals and society. It is crucial to determine if the information consumed on social media platforms isreal or fabricated. The proposed study demonstrates a few methodologies to identify false information from real one utilizing Machine learning(ML) classifiers and Natural language processing(NLP) techniques.To determine the best model, a comparison is done between ML classifiers using performance evaluation measures such as Confusion Matrix, Accuracy, Precision, F1-score, and Recall. Further, for feature engineering techniques like CountVectorizer, TF-IDF are employed.
Keywords - Machine learning, Social Media Misinformation Classification, Machine Learning Classifiers, Feature Engineering, Fake News on Social Media