Automated Analysis of Public Statements for Accuracy and Deception

In an era of information overload, the spread of misinformation by public figures has become a significant concern. Artificial Intelligence (AI) offers a powerful solution for detecting and analyzing fake statements through advanced Natural Language Processing (NLP) and machine learning techniques. This paper explores AI-driven fact-checking systems that assess the credibility of statements made by public figures by cross-referencing them with reliable data sources. The proposed approach involves real-time speech and text analysis, sentiment detection, and contextual verification using deep learning models and knowledge graphs. By automating the fact-checking process, AI can enhance public awareness, reduce the impact of misinformation, and promote accountability. However, challenges such as bias in AI models, data reliability, and ethical considerations must be addressed to ensure the effectiveness and fairness of such systems. This study provides insights into the methodologies, limitations, and future prospects of AI-powered fake statement detection. Index Terms — AI, ML, Regression, Multiple Regression, Logistic Regression, Linear Regression, Random Forest, Decision Tree, Sentiment Analysis, CNN, RNN, Naïve Bayes, Support Vector Machine (SVM), KNN, LSTM, Tokenization, Chatbot, Natural Language Processing (NLP), Feature Extraction.