179 research outputs found
Comparing Overlapping Data Distributions Using Visualization
We present results from a preregistered and crowdsourced user study where we
asked members of the general population to determine whether two samples
represented using different forms of data visualizations are drawn from the
same or different populations. Such a task reduces to assessing whether the
overlap between the two visualized samples is large enough to suggest similar
or different origins. When using idealized normal curves fitted on the samples,
it is essentially a graphical formulation of the classic Student's t-test.
However, we speculate that using more sophisticated visual representations,
such as bar histograms, Wilkinson dot plots, strip plots, or Tukey boxplots
will both allow people to be more accurate at this task as well as better
understand its meaning. In other words, the purpose of our study is to explore
which visualization best scaffolds novices in making graphical inferences about
data. However, our results indicate that the more abstracted idealized bell
curve representation of the task yields more accuracy.Comment: 16 pages, 8 figure
Visualization According to Statisticians: An Interview Study on the Role of Visualization for Inferential Statistics
Statisticians are not only one of the earliest professional adopters of data
visualization, but also some of its most prolific users. Understanding how
these professionals utilize visual representations in their analytic process
may shed light on best practices for visual sensemaking. We present results
from an interview study involving 18 professional statisticians (19.7 years
average in the profession) on three aspects: (1) their use of visualization in
their daily analytic work; (2) their mental models of inferential statistical
processes; and (3) their design recommendations for how to best represent
statistical inferences. Interview sessions consisted of discussing inferential
statistics, eliciting participant sketches of suitable visual designs, and
finally, a design intervention with our proposed visual designs. We analyzed
interview transcripts using thematic analysis and open coding, deriving
thematic codes on statistical mindset, analytic process, and analytic toolkit.
The key findings for each aspect are as follows: (1) statisticians make
extensive use of visualization during all phases of their work (and not just
when reporting results); (2) their mental models of inferential methods tend to
be mostly visually based; and (3) many statisticians abhor dichotomous
thinking. The latter suggests that a multi-faceted visual display of
inferential statistics that includes a visual indicator of analytically
important effect sizes may help to balance the attributed epistemic power of
traditional statistical testing with an awareness of the uncertainty of
sensemaking.Comment: 16 pages, 8 tables, 3 figure
Portrayal: Leveraging NLP and Visualization for Analyzing Fictional Characters
Many creative writing tasks (e.g., fiction writing) require authors to write
complex narrative components (e.g., characterization, events, dialogue) over
the course of a long story. Similarly, literary scholars need to manually
annotate and interpret texts to understand such abstract components. In this
paper, we explore how Natural Language Processing (NLP) and interactive
visualization can help writers and scholars in such scenarios. To this end, we
present Portrayal, an interactive visualization system for analyzing characters
in a story. Portrayal extracts natural language indicators from a text to
capture the characterization process and then visualizes the indicators in an
interactive interface. We evaluated the system with 12 creative writers and
scholars in a one-week-long qualitative study. Our findings suggest Portrayal
helped writers revise their drafts and create dynamic characters and scenes. It
helped scholars analyze characters without the need for any manual annotation,
and design literary arguments with concrete evidence
Supporting Comment Moderators in Identifying High Quality Online News Comments
ABSTRACT Online comments submitted by readers of news articles can provide valuable feedback and critique, personal views and perspectives, and opportunities for discussion. The varying quality of these comments necessitates that publishers remove the low quality ones, but there is also a growing awareness that by identifying and highlighting high quality contributions this can promote the general quality of the community. In this paper we take a user-centered design approach towards developing a system, CommentIQ, which supports comment moderators in interactively identifying high quality comments using a combination of comment analytic scores as well as visualizations and flexible UI components. We evaluated this system with professional comment moderators working at local and national news outlets and provide insights into the utility and appropriateness of features for journalistic tasks, as well as how the system may enable or transform journalistic practices around online comments
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