Online recruitment platforms such as LinkedIn, Glassdoor, Indeed.com, and naukri.com are used to build business connections, find jobs, and recruit Candidates. These platforms increase recruiter productivity, help reach wider audiences, and provide a rich source of information for job seekers. These platforms also witness enormous volumes of user-generated content such as job postings, CVs, and company profiles. Among this content, job postings are important for identifying, analyzing, and determining the roles, responsibilities and skills of a specific position. These act as a gateway for job seekers to understand the requirement and help the recruiter attract the right talent. But some of these contain untenable facts, vague, non-standard, and missing entities that dilute the content quality over the platforms. The unmonitored nature of this content makes it difficult to assess the information's credibility, affecting the platform's trustworthiness and, in turn, the user experience. Therefore, there is a need to identify, analyze, and enhance the content quality on these platforms. In this report, we look at different perspectives, literature, and work done for content quality analysis, detection and enhancement over online recruitment platforms.