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Social media platforms have become the primary medium for news consumption, offering vast amounts of real-time information. However, the proliferation of fake news across these platforms poses significant risks, including societal misinformation, political manipulation, and erosion of public trust. Traditional methods of combating fake news, such as manual fact-checking, have proven insufficient in curbing its spread due to the sheer volume of data and the speed at which misinformation can go viral. To address this challenge, artificial intelligence (AI) has emerged as a powerful tool in detecting fake news. Leveraging techniques such as natural language processing (NLP), machine learning algorithms, and deep learning, AI systems can analyze and flag deceptive content more efficiently than human-based efforts. This paper explores the influence of AI in identifying and mitigating fake news on social media platforms. It delves into how AI-driven fake news detection models work, examining the use of both supervised and unsupervised learning techniques. Additionally, the paper discusses the impact of these AI systems on user behavior, credibility assessment, and trust in social media platforms. However, while AI has shown significant promise, challenges such as algorithmic bias, ethical concerns, and the potential for misuse remain critical areas for future development. The integration of AI in fake news detection is reshaping the digital information landscape, offering both opportunities and risks for enhancing the quality of online discourse.
Shyam Swaroop T. (Wed,) studied this question.