Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

di Sciascio Maria Cecilia, Strohmaier David, Errecalde Marcelo Luis, Veas Eduardo Enrique

Interactive Quality Analytics of User-generated Content: An Integrated Toolkit for the Case of Wikipedia

ACM, 2019

Journal
Digital libraries and services enable users to access large amounts of data on demand. Yet, quality assessment of information encountered on the Internet remains an elusive open issue. For example, Wikipedia, one of the most visited platforms on the Web, hosts thousands of user-generated articles and undergoes 12 million edits/contributions per month. User-generated content is undoubtedly one of the keys to its success but also a hindrance to good quality. Although Wikipedia has established guidelines for the “perfect article,” authors find it difficult to assert whether their contributions comply with them and reviewers cannot cope with the ever-growing amount of articles pending review. Great efforts have been invested in algorithmic methods for automatic classification of Wikipedia articles (as featured or non-featured) and for quality flaw detection. Instead, our contribution is an interactive tool that combines automatic classification methods and human interaction in a toolkit, whereby experts can experiment with new quality metrics and share them with authors that need to identify weaknesses to improve a particular article. A design study shows that experts are able to effectively create complex quality metrics in a visual analytics environment. In turn, a user study evidences that regular users can identify flaws, as well as high-quality content based on the inspection of automatic quality scores.
2017

Strohmaier David, di Sciascio Maria Cecilia, Errecalde Marcelo, Veas Eduardo Enrique

WikiLyzer: Interactive Information Quality Assessment in Wikipedia

ACM Intelligent User Interfaces, 2017

Konferenz
Innovations in digital libraries and services enable users to access large amounts of data on demand. Yet, quality assessment of information encountered on the Internet remains an elusive open issue. For example, Wikipedia, one of the most visited platforms on the Web, hosts thousands of user-generated articles and undergoes 12 million edits/contributions per month. User-generated content is undoubtedly one of the keys to its success, but also a hindrance to good quality: contributions can be of poor quality because everyone, even anonymous users, can participate. Though Wikipedia has defined guidelines as to what makes the perfect article, authors find it difficult to assert whether their contributions comply with them and reviewers cannot cope with the ever growing amount of articles pending review. Great efforts have been invested in algorith-mic methods for automatic classification of Wikipedia articles (as featured or non-featured) and for quality flaw detection. However, little has been done to support quality assessment of user-generated content through interactive tools that allow for combining automatic methods and human intelligence. We developed WikiLyzer, a toolkit comprising three Web-based interactive graphic tools designed to assist (i) knowledge discovery experts in creating and testing metrics for quality measurement , (ii) users searching for good articles, and (iii) users that need to identify weaknesses to improve a particular article. A case study suggests that experts are able to create complex quality metrics with our tool and a report in a user study on its usefulness to identify high-quality content.
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