A General Multiview Framework for Assessing the Quality of Collaboratively Created Content on Web 2.0
|Daniel Hasan Dalip|
Marcos André Gonçalves
Marco Antônio Pinheiro de Cristo
Pável Pereira Calado
A General Multiview Framework for Assessing the Quality of Collaboratively Created Content on Web 2.0 - scientific work about quality in Web 2.0 documents published in 2017, written by Daniel Hasan Dalip, Marcos André Gonçalves, Marco Antônio Pinheiro de Cristo and Pável Pereira Calado.
User‐generated content is one of the most interesting phenomena of current published media, as users are now able not only to consume, but also to produce content in a much faster and easier manner. However, such freedom also carries concerns about content quality. In this work, authors propose an automatic framework to assess the quality of collaboratively generated content. Quality is addressed as a multidimensional concept, modeled as a combination of independent assessments, each regarding different quality dimensions. Accordingly, authors adopt a machine‐learning (ML)‐based multiview approach to assess content quality. We perform a thorough analysis of our framework on two different domains: Questions and Answer Forums and Collaborative Encyclopedias. This allowed us to better understand when and how the proposed multiview approach is able to provide accurate quality assessments. Their main contributions are: (a) a general ML multiview framework that takes advantage of different views of quality indicators; (b) the improvement (up to 30%) in quality assessment over the best state‐of‐the‐art baseline methods; (c) a thorough feature and view analysis regarding impact, informativeness, and correlation, based on two distinct domains.
To build the quality model authors used support vector regression (SVR).