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

@article{Momeni2016ASO,
  title={A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web},
  author={Elaheh Momeni and Claire Cardie and Nicholas A. Diakopoulos},
  journal={ACM Computing Surveys (CSUR)},
  year={2016},
  volume={48},
  pages={1 - 49}
}
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… 

Figures from this paper

How to Assess and Rank User-Generated Content on Web

TLDR
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.

Leveraging Semantic Facets for Adaptive Ranking of Social Comments

TLDR
This work proposes an adaptive faceted ranking framework which enriches comments along multiple semantic facets, thus enabling users to explore different facets and select combinations of facets in order to extract and rank comments that match their interests.

Personalized Review Ranking for Improving Shopper's Decision Making: A Term Frequency based Approach

TLDR
This paper constructed user profiles based on user's personal web trails, recent shopping history and previous reviews, incorporated user profiles into the authors' ranking algorithm, and assigned higher ranks to reviews that address individual shopper's concerns to the largest extent and leveraged user profiles to recommend products based on reviews texts.

Personalized Review Recommendation based on Users’ Aspect Sentiment

TLDR
An aspect sentiment similarity-based personalized review recommendation model (A2SPR), which quantifies review helpfulness and recommends reviews that are customized for each individual, and analyzes users’ aspect preferences from reviews and improves user similarity with users' fine-grained sentiment and product relevance.

An Approach for Time-aware Domain-based Social Influence Prediction

TLDR
This paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods and validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

CredSaT: Credibility ranking of users in big social data incorporating semantic analysis and temporal factor

TLDR
The CredSaT (Credibility incorporating Semantic analysis and Temporal factor), which is a fine-grained credibility analysis framework for use in big social data, is suggested, which includes a novel metric that includes both new and current features, as well as the temporal factor, to establish the credibility ranking of users.

Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums

TLDR
The problem is motivated and a publicly available annotated English corpus is created by crowdsourcing and a large set of features to predict the credibility of the answers are proposed, showing that the credibility labels can be predicted with high performance according to several standard IR ranking metrics.

Credibility Analysis in Social Big Data

TLDR
This chapter presents an overview of the notion of credibility in the context of SBD and lists an array of approaches to measure and evaluate the trustworthiness of users and their contents.

Time-aware domain-based social influence prediction

TLDR
The aim of this paper is to determine domain-based social influencers by means of a framework that incorporates semantic analysis and machine learning modules to measure and predict users’ credibility in numerous domains at different time periods.

References

SHOWING 1-10 OF 115 REFERENCES

Finding high-quality content in social media

TLDR
This paper introduces a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition, and shows that its system is able to separate high-quality items from the rest with an accuracy close to that of humans.

YR-2007-005 FINDING HIGH-QUALITY CONTENT IN SOCIAL MEDIA WITH AN APPLICATION TO COMMUNITY-BASED QUESTION ANSWERING

TLDR
This paper introduces a general classification framework for combining the evidence from different sources of information that can be tuned automatically for a given social media type and quality definition, and shows that its system is able to separate high-quality items from the rest with an accuracy close to that of humans.

Designing novel review ranking systems: predicting the usefulness and impact of reviews

TLDR
It is shown that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales and the results can have several implications for the market design of online opinion forums.

How opinions are received by online communities: a case study on amazon.com helpfulness votes

TLDR
It is found that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product.

Exploiting social context for review quality prediction

TLDR
A generic framework for incorporating social context information by adding regularization constraints to the text-based predictor is proposed and has the advantage that the resulting predictor is usable even when social context is unavailable.

Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics

TLDR
This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.

Finding Credible Information Sources in Social Networks Based on Content and Social Structure

  • K. CaniniB. SuhP. Pirolli
  • Computer Science
    2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing
  • 2011
TLDR
The study indicates that both the topical content of information sources and social network structure affect source credibility, and designs a novel method of automatically identifying and ranking social network users according to their relevance and expertise for a given topic.

Tag ranking

TLDR
This paper proposes a tag ranking scheme, aiming to automatically rank the tags associated with a given image according to their relevance to the image content, and applies tag ranking into three applications: tag-based image search, tag recommendation, and group recommendation.

Properties, Prediction, and Prevalence of Useful User-Generated Comments for Descriptive Annotation of Social Media Objects

TLDR
This work uses standard machine learning methods to develop a “usefulness” classifier, exploring the impact of surface-level, syntactic, semantic, and topic-based features in addition to extra-linguistic attributes of the author and his or her social media activity to investigate patterns in the commenting culture of two popular social media platforms.

Identification of useful user comments in social media: a case study on flickr commons

TLDR
The notion of usefulness in the context of social media comments is discussed and compared from end-users as well as expertusers perspectives and a machine-learning approach is presented to automatically classify comments according to their usefulness.
...