A new paper on visualizing social media has been released on the University of Maryland, Human Computer Interaction Laboratory tech report archive. Co-authored by Derek Hansen, myself, and Ben Shneiderman, the paper describes and visualizes the patterns of connections formed when people tweet about events like conferences and news stories. EventGraphs_2010_HCIL_Tech_Report http://www.cs.umd.edu/localphp/hcil/tech-reports-search.php?number=2010-13 Hansen, D., Smith, [...]
I will speak about social media networks on October 24th, 2013 at the department of Computer Science at the Arizona State University.
The graph represents a network of 712 Twitter users whose recent tweets contained “@ASU”, taken from a data set limited to a maximum of 10,000 tweets. The network was obtained from Twitter on Sunday, 13 October 2013 at 19:56 UTC.
The tweets in the network were tweeted over the 4-day, 21-hour, 47-minute period from Tuesday, 08 October 2013 at 21:48 UTC to Sunday, 13 October 2013 at 19:35 UTC.
There is an edge for each “replies-to” relationship in a tweet. There is an edge for each “mentions” relationship in a tweet. There is a self-loop edge for each tweet that is not a “replies-to” or “mentions”.
The graph is directed.
The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values