User-generated content (UGC) on the Web, especially on social media platforms, facilitates the association of additional information with digital resources and online social topics and it can provide valuable supplementary content. However, UGC varies in quality and, consequently, raises the challenge of how to maximize its utility for a variety of end-users, in particular in the age of misinformation. This study aims to provide researchers and Web data curators with answers to the following questions: (1) What are the existing approaches and methods for assessing and ranking UGC (2) What features and metrics have been used successfully to assess and predict UGC value across a range of application domains This survey is composed of a systematic review of approaches for assessing and ranking UGC: results obtained by identifying and comparing methodologies within the context of short text-based UGC on the Web. This survey categorizes existing assessment and ranking approaches into four framework types and discusses the main contributions and considerations of each type. Furthermore, it suggests a need for further experimentation and encourages the development of new approaches for the assessment and ranking.