Interactive visualization for news readers' beliefs: Why and how
Many data journalists perceive their goal to be presenting data as clearly as possible. But a single data set provides only a partial view on what's true. Readers' prior beliefs about a topic can — and should — influence what they conclude from new data. Visualizations can be used to gather and represent beliefs in ways that transform the reader's experience and your own journalistic design process.
This talk will introduce you to techniques and theories for helping your readers understand, What do (and should I) believe? We'll demo a new tool that allows anyone to create a "You draw it" style visualization to elicit and visualize readers' predictions.
Jessica Hullman is an Assistant Professor in Computer Science and Journalism at Northwestern. The goal of her research is to develop computational tools and techniques that improve how people reason with data. Most of her current research focuses on how understandable presentations of uncertainty and interactive visualizations that enable users to articulate and reason with prior beliefs can transform how lay people and analysts alike interact with data.
Yea-Seul Kim is a PhD Candidate in the Information School at University of Washington. She is interested in developing both algorithms and interfaces that make data more accessible to help people of varying levels of expertise better understand data.
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