All author's publications are listed below.
Information visualization could be used to leverage the credibility of displayed scientific data. However, little was known about how display characteristics interact with individuals' predispositions to affect perception of data credibility. Using an experiment with 517 participants, we tested perceptions of data credibility by manipulating data visualizations related to the issue of nuclear fuel cycle based on three characteristics: graph format, graph interactivity, and source attribution. Results showed that viewers tend to rely on preexisting levels of trust and peripheral cues, such as source attribution, to judge the credibility of shown data, whereas their comprehension level did not relate to perception of data credibility. We discussed the implications for science communicators and design professionals.
Research suggests non-experts associate different content with the terms “global warming” and “climate change.” We test this claim with Twitter content using supervised learning software to categorize tweets by topic and explore differences between content using “global warming” and “climate change” between 1 January 2012 and 31 March 2014. Twitter data were combined with temperature records to observe the extent to which temperature was associated with Twitter discussions. We then used two case studies to examine the relationship between extreme temperature events and Twitter content. Our findings underscore the importance of considering climate change communication on social media.