Abstract. This thesis introduces Article Enhancer, a semi-automated web application that utilizes crowdsourcing services, specifically Amazon’s Mechanical Turk platform, for augmenting articles with various referencing content gathered from the crowd-workers, on demand. The main goal of Article Enhancer is to address the question of how scientific articles can be made more credible, before dissemination to the public. This application serves as a tool in helping users find suitable supporting content for their articles in a novel way, removing all the manual work of doing it themselves.
Media literacy, social media, fake news and crowdsourcing are discussed as part of related work. Also, tools that offer a similar functionality are reviewed. Furthermore, system design and implementation for Article Enhancer is presented. It is important to mention that the referencing content provided through Article Enhancer comes from already existing online content. Although Article Enhancer is semi-automated system, its strongest point compared to the other systems, is that it doesn’t require extra human effort to enrich articles especially with visualization content, and providing already existing content on the web avoiding the process of creating new content, making it a fresh approach in this line of software service.
To evaluate Article Enhancer, we deployed the web app in a real-life setting, a space oriented towards students known as Tellus, at the University of Oulu. This testing proceedings helped in determining that the system appears alluring and attractive to new users. Article Enhancer proved to be unique and thrilling after the first encounter for many of the users. Feedback also shows that adding and embedding content is an innovative way to make articles become more credible in the eye of the reader
