Abstract: The challenge posed by misinformation is critical because it confuses public perception and undermines trust in the traditional news ecosystem, which has accuracy and truth as cores. To combat this rapid spreading fake news, we propose a tool that detects and classifies information posted on social media as false information. This system analyzes user-submitted text by cross-referencing it with verified data from trusted repositories. Based on this analysis, the model categorizes the content as either authentic or fabricated with clear labeling in its output. This solution not only enhances the detection of misleading content but also bolsters public trust and reduces the damaging effects of false information.

Keywords: False information detection, semantic analysis, sentence-level features, disinformation, text categorization

| DOI: 10.17148/IARJSET.2024.111114