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A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web | ACM Computing Surveys

survey

A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web

Authors:

Nicholas DiakopoulosAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 48, Issue 3

Article No.: 41, Pages 1 - 49

https://doi.org/10.1145/2811282

Published: 22 December 2015 Publication History

Abstract

User-generated content (UGC) on the Web, 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 and Web 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.

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Hsieh YLu L(2025)Trustworthiness evaluation of reviews for products with their traits in e-commercial communitiesAsia Pacific Journal of Marketing and Logistics10.1108/APJML-12-2024-2007(1-31)Online publication date: 15-Aug-2025
https://doi.org/10.1108/APJML-12-2024-2007
Xu XZhang YZhou FSong JWalsh TShah JKolter Z(2025)Improving multimodal social media popularity prediction via selective retrieval knowledge augmentationProceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v39i1.32078(932-940)Online publication date: 25-Feb-2025
https://dl.acm.org/doi/10.1609/aaai.v39i1.32078
Suson RAtibing NEvangelista SWenceslao CMaturan FVillarosa ROcampo L(2024)Quasi-experimental quality evaluation of educational-purposed user-generated contents under a stochastic multi-criteria environmentApplied Artificial Intelligence10.1080/08839514.2024.238950138:1Online publication date: 25-Aug-2024
https://doi.org/10.1080/08839514.2024.2389501
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A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web

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