A survey on assessment and ranking methodologies for user-generated content on the web

Elaheh Momeni, Claire Cardie, Nicholas Diakopoulos

Research output: Contribution to journalReview articlepeer-review

16 Scopus citations


User-generated content (UGC) on theWeb, especially on social media platforms, facilitates the association of additional information with digital resources; thus, 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. This study aims to provide researchers andWeb data curators with comprehensive answers to the following questions: What are the existing approaches and methods for assessing and ranking UGC? What features and metrics have been used successfully to assess and predict UGC value across a range of application domains? What methods can be effectively employed to maximize that value? This survey is composed of a systematic review of approaches for assessing and ranking UGC: results are obtained by identifying and comparing methodologies within the context of short text-based UGC on the Web. Existing assessment and ranking approaches adopt one of four framework types: the community-based framework takes into consideration the value assigned to content by a crowd of humans, the end-user-based framework adapts and personalizes the assessment and ranking process with respect to a single end-user, the designer-based framework encodes the software designer's values in the assessment and ranking method, and the hybrid framework employs methods from more than one of these types. This survey suggests a need for further experimentation and encourages the development of new approaches for the assessment and ranking of UGC.

Original languageEnglish (US)
Article number2811282
JournalACM Computing Surveys
Issue number3
StatePublished - Dec 1 2015


  • Adaptive
  • Assessment
  • Human centered
  • Interactive
  • Machine centered
  • Ranking
  • Social media
  • User-generated content

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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