This article examines the potential of artificial intelligence (AI) technologies in combating the proliferation of fake news across digital media platforms. As misinformation continues to spread rapidly through social media networks, traditional fact-checking methods have proven insufficient to address the scale and speed of false information dissemination. This study explores various AI-driven approaches, including natural language processing, machine learning algorithms, and deep learning models, to detect, classify, and mitigate fake news content. Through a comprehensive analysis of existing AI detection systems and their effectiveness across different platforms including Facebook, Twitter (X), Instagram, and TikTok, this research reveals both the promising capabilities and inherent limitations of AI-based solutions. The findings demonstrate that while AI systems achieve significant accuracy rates in identifying misinformation (ranging from 78% to 94% depending on the model and context), challenges remain in handling context-dependent content, satirical material, and evolving misinformation tactics. The study also addresses ethical considerations surrounding AI deployment in content moderation, including concerns about censorship, bias, and the balance between automated detection and human oversight. The research concludes that while AI represents a powerful tool in the fight against fake news, a hybrid approach combining AI capabilities with human expertise and platform policy reforms offers the most promising path forward for maintaining information integrity in the digital age.
Zaza Tsotniashvili (Mon,) studied this question.
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