The social media technologies are open to users who are intended in creating a community and publishing their opinions of recent incidents. The participants of the online social networking sites remain ignorant of the criticality of disclosing personal data to the public audience. The private data of users are at high risk leading to many adverse effects like cyberbullying, identity theft, and job loss. This research work aims to define the user entities or data like phone number, email address, family details, health-related information as user’s sensitive private data (SPD) in a social media platform. The proposed system, Tweet-Scan-Post (TSP), is mainly focused on identifying the presence of SPD in user’s posts under personal, professional, and health domains. The TSP framework is built based on the standards and privacy regulations established by social networking sites and organizations like NIST, DHS, GDPR. The proposed approach of TSP addresses the prevailing challenges in determining the presence of sensitive PII, user privacy within the bounds of confidentiality and trustworthiness. A novel layered classification approach with various state-of-art machine learning models is used by the TSP framework to classify tweets as sensitive and insensitive. The findings of TSP systems include 201 Sensitive Privacy Keywords using a boosting strategy, sensitivity scaling that measures the degree of sensitivity allied with a tweet. The experimental results revealed that personal tweets were highly related to mother and children, professional tweets with apology, and health tweets with concern over the father’s health condition.

社交媒体技术向旨在创建社区并发布他们对最近事件的意见的用户开放。在线社交网站的参与者仍然不知道向公众披露个人数据的重要性。用户的私人数据处于高风险中，导致许多不利影响，如网络欺凌、身份盗用和失业。本研究工作旨在将用户实体或数据（如电话号码、电子邮件地址、家庭详细信息、健康相关信息）定义为社交媒体平台中的用户敏感私人数据 (SPD)。所提议的系统 Tweet-Scan-Post (TSP) 主要侧重于识别个人、专业和健康领域下用户帖子中 SPD 的存在。TSP 框架基于社交网站和 NIST、DHS、GDPR 等组织制定的标准和隐私法规而构建。提议的 TSP 方法解决了在机密性和可信度范围内确定敏感 PII 的存在、用户隐私的普遍挑战。TSP 框架使用具有各种最先进机器学习模型的新型分层分类方法将推文分类为敏感和不敏感。TSP 系统的发现包括使用提升策略的 201 个敏感隐私关键字，敏感度缩放衡量与推文相关的敏感程度。实验结果显示，个人推文与母子高度相关，专业推文与道歉，