Lariat: A Visual Analytics Tool for Social Media Researchers to Explore Twitter Datasets

Chen, Nan-Chen
Brooks, Michael
Kocielnik, Rafal
Hong, Sungsoo (Ray)
Smith, Jeff
Lin, Sanny
Qu, Zening
Aragon, Cecilia
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Online social data is potentially a rich source of insight into human behavior, but the sheer size of these datasets requires specialized tools to facilitate social media research. Visual analytics tools are one promising approach, but calls have been made for more in-depth studies in specific application domains to contribute to the design of such tools. We conducted a formative study to better understand the needs of social media researchers, and created Lariat, a visual analytics tool that facilitates exploratory data analysis through integrated grouping and visualization of social media data. The design of Lariat was informed by the results of the formative study and sensemaking theory, both indicating that the exploratory processes require search, comparison, verification, and iterative refinement. Based on our results and the evaluation of Lariat, we identify a number of design implications for future visual analytics tools in this domain.
social media, user research, visual analytics, visual exploration, visualization
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