Improving the quality of user-generated content

Abstract

User-generated content is a huge source of information in the modern world. The rise of social media and other user-generated content platforms has enabled the public to create and share an increasing amount of information with others. However, as people share the information with no credible background, the reliability of user-generated content is questionable, and the quality of data and information is uncertain.

This thesis aims to study the underlying issues that reduce user-generated content's data and information quality and presents a solution to improve the overall quality of content. The problems are surveyed through literature and examining existing usergenerated content platforms. Most issues relate to using the public as the content provider and not having any proper design decisions to overcome data and information quality flaws. Additionally, the definitions of data and information quality used in existing research are imperfect for the domain of user-generated content, and there is a need for establishing definitions solely for user-generated content.

This research proposes new definitions for data and information quality and presents a platform design that considers the quality of content during design and development to improve the data and information quality of user-generated content. The design enhances the information collection and data curation processes to procure higher quality content from users.

The research has three significant contributions: (1) A comprehensive set of data and information quality characteristics for user-generated content, (2) extension of the development life cycle with data and information quality characteristics for usergenerated content platforms, and (3) a framework that integrates quality characteristics into the design to store and assess the reliability and quality of user-generated content.