Web Science and Digital Libraries Research Group

Figure 1: Screenshot of 24 of 27 Democratic candidates  hopeful of the Presidential nomination on May 25, 2019 (Source: The Washington Post) In our previous post,  Twitter Follower Growth for the 2020 Democratic Candidates , we used the Twitter follower growth between 2019-01-01 and 2019-08-23 as a proxy to measure popularity of the Democratic candidates and categorized the campaign of each candidate based on their absolute Twitter follower growth into four categories: already popular, big winners, nobody noticed, and beneficial .  Since our previous post, all the candidates except Joe Biden, the presumptive presidential nominee, have dropped out. In this post, we revisit the Twitter follower growth of all 27 Democratic candidates by extending our study to between 2019-01-01 and 2020-04-18. We plotted an  interactive D3 Twitter follower graph to show the trends in the absolute increase of the followers for each candidate .    Twitter and Web Archives Forbes, in their ar

(Previous semester Information Visualization highlights posts: Fall 2017 , Spring 2017 , Spring 2016 , Spring 2015 , Spring/Fall 2013 , Fall 2012 , Fall 2011 ) In Fall 2019, I introduced CS 625: Data Visualization , a new graduate-level visualization course. (This course was taught in a flipped+hybrid manner, as I described in an earlier blog post .) We used the same textbook, Tamara Munzner's Visualization Analysis and Design , as in my previous CS 725/825 Information Visualization courses, but this course was designed to be a gentler introduction to visualization and data analysis. We focused on basic visualization design principles and on how to ask good questions rather than D3 programming. Students were allowed to use whatever tool they wished, but I emphasized clear design no matter what tool was used. Over the course of two assignments ( HW7 , HW8 ), students developed questions about real-world data, developed a draft visualization, and then refined the visualization based

This image by NASA is licensed under NASA's Media Usage Guidelines In Part 1 , we introduced Hypercane , a tool for automatically sampling mementos from web archive collections. Web archive collections consist of thousands of documents, and humans need tools to intelligently select mementos for a given purpose. Hypercane's goal is to supply us with a list of memento URI-Ms derived from the input we provide. In Part 2 , I highlighted how Hypercane's synthesize action converts its input into other formats like JSON for Raintale stories, WARCs for Archives Unleashed Toolkit , or boilerplate-free files for Gensim . This post focuses on the primitive advanced actions that make up Hypercane's sampling algorithms. We can mix and match different primitives to arrive at the sample that best meets our needs. The DSA project 's goal is to summarize a web archive collection by selecting a small number of exemplars and then visualize them with social media stor