Abstract Generative artificial intelligence technologies have evolved at a remarkable pace, flooding social media platforms with machine-created content ranging from written posts an‎d digital images to audio clips an‎d video productions. These AI tools offer exciting possibilities for creativity an‎d productivity, yet they simultaneously raise serious concerns about the spread of false information, violations of academic honesty, challenges in media authentication, an‎d erosion of public confidence . The growing sophistication of AI-generated materials makes them nearly indistinguishable from genuine human work, turning the identification of synthetic content into an urgent area of study. This research provides a comprehensive examination of current scholarship focused on identifying AI-generated materials within social media an‎d other digital spaces. By analyzing a broad collection of academic studies, the paper investigates detection techniques applicable to different content types, encompassing written text, visual imagery, an‎d video recordings. Researchers in this field have applied diverse analytical methods, including examination of language patterns, implementation of machine learning algorithms, deployment of deep neural networks, utilization of vision-language comparison models, analysis of user behavior patterns, an‎d integration of multiple forensic approaches . Particular emphasis is placed on emerging techniques that analyze meaning-level content features, maintain effectiveness against manipulation attempts, perform reliably across different contexts, an‎d ensure equitable detection outcomes . By comparing an‎d categorizing previous research findings, this investigation reveals both the capabilities an‎d shortcomings of current detection methods while pinpointing major obstacles such as circumvention tactics, algorithmic prejudice, implementation at scale, an‎d the continuous arms race between content generation an‎d detection technologies. The study wraps up by discussing unexplored research opportunities an‎d potential pathways forward, stressing the importance of comprehensive, content-type-sensitive, an‎d socially aware detection systems to keep pace with the changing nature of AI-generated content on social platforms .

2/18/2026 12:00:00 AM

89743

1404/11/29

Detecting AI-Generated Content in Social Media

AI-generated content; content authenticity , social media analysis , generative artificial intelligence , content detection , machine learning