10.46243/jst.2024.v9.i1.pp39-49 registered

With the widespread adoption of the internet, online interactions have become an integral part of modern communication. However, this surge in digital interactions has also brought about a significant rise in deceptive practices, ranging from misinformation and fraud to identity theft and cyberbullying. Detecting and mitigating these dishonest behaviors has become a critical concern for maintaining trust and integrity in digital communities. The primary challenge lies in developing a robust and automated system capable of identifying deceptive content amidst the vast volume of online interactions. In the absence of advanced AI-based systems, deception detection in online interactions has heavily relied on manual monitoring, keyword-based filters, and rule-based algorithms. These conventional methods are limited in their effectiveness, as they struggle to adapt to evolving deceptive tactics and often generate false positives or negatives. Therefore, the need for effective deception detection systems in online interactions has never been more pressing. The advent of social media, e-commerce, and various online forums has created an environment where deceptive practices can have far-reaching consequences. Ensuring the safety and trustworthiness of these platforms is imperative for user confidence, cybersecurity, and the overall well-being of online communities. Hence, by utilizing machine learning algorithms, advanced linguistic analysis, and behavioral pattern recognition, this research aims to develop a powerful tool capable of accurately discerning deceptive from genuine online interactions. Through the integration of multi-modal approaches and feature engineering, the proposed system promises to significantly enhance the accuracy and efficiency of deception detection in digital communities, ultimately fostering a safer and more trustworthy online environment