Sung-Hee Kim

Deokgun Park, Sung-Hee Kim, Niklas Elmqvist

Overplotting of data points is a common problem when visualizing large datasets in a scatterplot, particularly when mapping nominal dimensions to one of the scatterplot axes. Transparency, aggregation, and jittering have previously been suggested to address this issue, but these solutions all have drawbacks for assessing the data distribution in the plot. We propose gatherplots, an extension of scatterplots that eliminates overplotting, particularly for nominal variables. In gatherplots, every data point that maps to the same position coalesces to form a stacked entity, thereby making it easier to compare the absolute and relative sizes of data groupings. The size and aspect ratio of data points can also be changed dynamically to make it easier to compare the composition of different groups. Furthermore, several embedded interaction techniques support slicing and dicing the gatherplot by pivoting on particular dimensions, ranges, and values in the dataset. Our evaluation shows that gatherplots enable users from the general public to judge the relative portion of subgroups more quickly and more correctly than when using conventional scatterplots with jittering. Furthermore, a review conducted by a group of visualization experts evaluated and commented on the gatherplot design.

Jina Huh, Bum Chul Kwon, Jaegul Choo et al.

As online health communities (OHCs) grow, users find it challenging to properly search, read, and contribute to the community because of its overwhelming content. Our goal is to understand OHC users' needs and requirements for better delivering large-scale OHC content. We interviewed 14 OHC users with interests in diabetes to investigate their attitudes and needs towards using OHCs and 2 OHC administrators to assess our findings. Four personas -Coddlers, Scientists, Adventurers, and Opportunists- emerged, which inform users' interaction behavior and attitudes with OHCs. An individual can possess the characteristics of multiple personas, which can also change over time. Our personas uniquely describe users' OHC participation intertwined with illness contexts compared to existing social types in general online communities. We discuss broader implications back to the literature and how our findings apply to other illness contexts in OHCs. We end with requirements for personalized delivery of large-scale OHC content.