Andreas Breiter

Gabriela Molina León, Michael Lischka, Andreas Breiter

Choropleth maps are among the most common visualization techniques used to present geographical data. These maps require an equal-area projection but there are no clear criteria for selecting one. We collaborated with 20 social scientists researching on the Global South, interested in using choropleth maps, to investigate their design choices according to their research tasks. We asked them to design world choropleth maps through a survey, and analyzed their answers both qualitatively and quantitatively. The results suggest that the design choices of map projection, center, scale, and color scheme, were influenced by their personal research goals and the tasks. The projection was considered the most important choice and the Equal Earth projection was the most common projection used. Our study takes the first substantial step in investigating projection choices for world choropleth maps in applied visualization research.

Hendrik Heuer, Andreas Breiter

People are increasingly consuming news curated by machine learning (ML) systems. Motivated by studies on algorithmic bias, this paper explores which recommendations of an algorithmic news curation system users trust and how this trust is affected by untrustworthy news stories like fake news. In a study with 82 vocational school students with a background in IT, we found that users are able to provide trust ratings that distinguish trustworthy recommendations of quality news stories from untrustworthy recommendations. However, a single untrustworthy news story combined with four trustworthy news stories is rated similarly as five trustworthy news stories. The results could be a first indication that untrustworthy news stories benefit from appearing in a trustworthy context. The results also show the limitations of users' abilities to rate the recommendations of a news curation system. We discuss the implications of this for the user experience of interactive machine learning systems.