2013/06/17 by Daniel Archambault, Derek Greene, Archambault, Daniel +3
Computer Science · Physics and Astronomy · #Advanced Text Analysis Techniques #Complex Network Analysis Techniques #Data Visualization and Analytics #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1306.3839
19 pages, colour figures
arxiv created 2013/06/17 · openalex publication_date 2013/06/17 · arxiv updated 2013/06/18 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Analysts and social scientists in the humanities and industry require techniques to help visualize large quantities of microblogging data. Methods for the automated analysis of large scale social media data (on the order of tens of millions of tweets) are widely available, but few visualization techniques exist to support interactive exploration of the results. In this paper, we present extended descriptions of ThemeCrowds and SentireCrowds, two tag-based visualization techniques for this data. We subsequently introduce a new list equivalent for both of these techniques and present a number of case studies showing them in operation. Finally, we present a formal user study to evaluate the effectiveness of these list interface equivalents when comparing them to ThemeCrowds and SentireCrowds. We find that discovering topics associated with areas of strong positive or negative sentiment is faster when using a list interface. In terms of user preference, multilevel tag clouds were found to be more enjoyable to use. Despite both interfaces being usable for all tested tasks, we have evidence to support that list interfaces can be more efficient for tasks when an appropriate ordering is known beforehand.