ML-BASED ANALYTICAL STUDY ON THE LEVEL OF SECURITY OF USER DATA ON SOCIAL MEDIA WEBSITE

Abstract

The fast development of social media sites has transformed the way people interact and share information, yet it has also created massive concerns regarding the safety of user data. As social media users share personal data daily (in billions), social media companies have the responsibility of ensuring this information is not hacked, accessed by unauthorized individuals, or used in different ways. This paper looks at the present condition of the security of user data on the largest social media sites, the efficiency of the security measures that can be applied, the most frequently observed types of vulnerabilities, and the contribution of user behavior to data compromise. The study examines the extent to which social media platforms enhance the safety of sensitive information through a general assessment of security measures, case studies of data breaches, and user surveys and indicates aspects where social media could be deficient in security. Another aspect that the research covers is the trade-off in the safeguarding of data and the functionality of the platform, whether the existing regulations and self-enforced security standards are adequate. The results provide an indication that although certain progress has been made, the challenges of ensuring strong data protection still exist. The paper ends with a conclusion based on some recommendations on how to improve data security on social media, such as enhanced user awareness, enhanced encryption, and stricter regulatory control measures.