With the rapid growth of the internet, online interactions have become a cornerstone of modern communication. However, this increase in digital engagement has also led to a notable rise in deceptive practices, including misinformation, fraud, identity theft, and cyberbullying. Detecting and addressing these dishonest behaviors is crucial for preserving trust and integrity within digital communities. A significant challenge lies in developing a robust, automated system capable of identifying deceptive content amid the vast volume of online interactions. Traditionally, deception detection has depended on manual monitoring, keyword-based filters, and rule-based algorithms, which are often inadequate. These conventional methods struggle to adapt to evolving deceptive tactics and frequently produce false positives and negatives. As social media, e-commerce, and online forums continue to expand, the implications of deceptive practices grow more serious, underscoring the need for effective deception detection systems. Ensuring the safety and trustworthiness of these platforms is vital for user confidence, cybersecurity, and the overall well-being of online communities. This research seeks to address this pressing need by leveraging machine learning algorithms, advanced linguistic analysis, and behavioral pattern recognition to develop a sophisticated tool for accurately discerning deceptive interactions from genuine ones. By integrating multi-modal approaches and feature engineering, the proposed system aims to significantly improve the accuracy and efficiency of deception detection, ultimately fostering a safer and more trustworthy online environment.

With the rapid growth of the internet, online interactions have become a cornerstone of modern communication