Helping readers make sense of data Visualizations
I designed enhancements to news articles alongside other interns from the UCSD Design Lab. The enhancements consist of multiple features that would help readers make sense of visualizations while increasing engagement and contribution with data centered articles.
June - Dec 2021
(6 months)
UX Designer
Front-end Developer
Ifrah
Kevin
Ritvik
Figma
React
Javascript
User research showed that we needed to improve the experience of commenting systems on data centered articles.
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The San Diego Union-Tribune Comments
Readers need to scroll through the entire comment section to find out if the topic or questions surrounding a topic has been brought up in discussion. The comment sections in many data centered articles are structured this way
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A reader who is new to data centered articles may find the visualizations overwhelming. They may have questions pertaining to a certain visualization but their question may be lost in the surplus of meaningless comments.
The solution my team and I came up with is enhancements that surface relevant questions, resources, and experiences from the discussion. The features present the content from the discussion where it is relevant to the visualization. As the discussion continues around an article, the enhancements created on the visualization increase.
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Enhancements allow the nth reader to look back at what previous readers have entered, such as answered questions to get a better understanding of the visualization.
We found that 3/5 interviewees don't think commenting spaces added any value if they did not have adequate moderation.
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Online commenting systems often times opinion based allowing them to be a venue for attacks and misinformation.
The comments are not encouraging to new readers because of the language and "experts" going back and forth.
Guided by the user interviews I began design the enhancements, starting with the Question and Answer and User Stories features.
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Journalist, readers, instructors and moderators gave us feedback on our prototype.
Verbatim Feedback:
"Its kind of like in class when your in class and the teacher is like Hey as a question even if you think its stupid someone else probably has the same question"
"People will get a lot of different things out of data based on their level of data literacy"
"By Aggregating this information we get closer to fact based information"
"It can help minimize fake news"
Questions are sourced from the discussion and Answers from the community answers are summarized. The location of the icon signifies where on the visual the question is relevant to.
Responses containing reports and external sources are linked to the enhancement. This allows readers to gain further understanding of the topic through external resources.
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The experiences of the community are summarized. The data is more human centered by providing readers insight on the human experience at a certain time period.
The article enhancements serve as a means to add or takeaway from data visualizations. In the future we could dive deeper into why readers might find it overwhelming.
The current legend used to explain the enhancements takes up a lot screen space. In the future we could have a hide and show option so that it doesn't seem like part of the enhancements itself.
The project continued into the fall of 2021 to conduct more interviews and ultimately be entered into CHI 2022​.
Thanks for reading!
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