COMBATING MISINFORMATION WITH MACHINE LEARNING: TOOLS FOR TRUSTWORTHY NEWS CONSUMPTION

In today's era the issue of misinformation poses a challenge to public discussions and decision making processes. This study examines how machine learning (ML) models fare in detecting misinformation on online platforms using the LIAR dataset. By comparing unsupervised and deep learning methods the research aims to pinpoint the effective strategies for distinguishing between true and false information. Performance measures like accuracy, precision, recall, F1 score and AUC ROC curve are employed to evaluate each model's performance. The results indicate that ensemble models that combine ML techniques tend to outperform others by striking a balance between accuracy and the ability to detect forms of misinformation. This research contributes to endeavors in fostering digital spaces by enhancing ML tools capabilities, in identifying and curbing the spread of false information.