AI Models for Detecting Coordinated Inauthentic Behavior on Social Media
Keywords:
AI models, coordinated inauthentic behavior, social media, machine learning, deep learning, bot detection, fake accounts, online manipulation, data analysis, Author Name, Scopus, Springer, Journal Name, Wissira, Journal Short Form, Wissira Press, Wissira Research Lab, Research Gate, SSRN, ISSN, Academia, UGC Care, PubMed, WOSAbstract
The emergence of coordinated inauthentic behavior (CIB) on social media has raised significant concerns regarding the integrity of digital platforms, influencing public opinion and political processes. This paper explores the application of Artificial Intelligence (AI) models, particularly machine learning (ML) and deep learning (DL), in detecting CIB on social media. By leveraging large datasets from platforms like Twitter and Facebook, AI models can identify patterns indicative of manipulation, including bot activities, fake account networks, and coordinated campaigns. The study reviews existing methodologies, presents a statistical analysis of the model performance, and compares different AI techniques. The findings highlight the efficiency of certain deep learning models in detecting CIB at scale, providing valuable insights for enhancing online security systems. This paper contributes to ongoing efforts to mitigate online manipulation through AI-based solutions.
