Exploring interactive features in auto-generated articles through article visualization

Abstract: News articles generated by artificial intelligence rather than human reporters are referred to as automated journalism. This thesis explores how to create a trustworthy representation of news articles that mainly are generated by algorithmic decisions. The hypothesis of this thesis takes the background (characteristics of the underlying system design) and the foreground (millennials news consumption behaviour) contexts into consideration in order to provide an optimal approach for trustworthy representation of auto-generated articles. A theory about algorithmic transparency in the news media has been investigated to reveal information about the systems selection processes. The principles of glanceability and the heuristic principles are applied to proposed design solutions (interactive features). The outcomes show that newsreaders are positive towards a system that is trying to encourage them to fact-check the articles. Additionally, the outcomes also contributed to the understanding of how newsreaders can consume auto-generated news.