Social media networks are a vital platform for the virtual community, connecting billions of people for mutual interaction. However, hackers are aggressively exploiting these platforms for malicious intentions. Despite the implementation of preventive measures, hacker activity has surged, leading to the need for a social media intrusion detection system. Online social networks have provided users with conveniences but also pose significant threats to their security and privacy. Users' attempts to adjust their privacy settings are less than their efforts to implement other security measures. A significant proportion of individuals using social media platforms have limited technical expertise, resulting in less apprehension about the privacy implications of their personal content. To address privacy concerns, a comprehensive set of well- defined policies should be established, including robust passwords, periodic password changes, caution when sharing personal information, the importance of antivirus software, and proprietary software use. Machine learning algorithms can be employed to examine user sentiment, identify deceptive news, and combat child trafficking. Researchers are currently investigating the incorporation of improving cyber security of social media platforms by using artificial intelligence, focusing particularly on adversarial machine learning. The growing popularity of AI for Good project and the emphasis on Fair AI and Bias in AI highlight the need for further research on how these fields can be used in relation to the social media. This research provides a thorough analysis of the most recent advancements in social media security and dependability, presenting a groundbreaking approach to enhance security and dependability. Organizations must safeguard information broadcasted on social media due to frequent security breaches, which can hinder economic growth.
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