Web Science and Digital Libraries Research Group

https://xkcd.com/2180/ A record six WS-DL courses are offered for Fall 2019: CS 418/518 Web Programming , Dr. Jian Wu , Tuesdays & Thursdays, 1:30-2:45pm Topics : LAMP (Linux, Apache, MySQL, PHP), jQuery, git/GitHub  CS 431/531 Web Server Design , Sawood Alam, Thursdays, 4:20-7:00pm Topics : HTTP, REST (Representational State Transfer), HATEOAS  CS 620 Introduction to Data Science , Dr. Sampath Jayarathna , Tuesdays, 4:20-7:00pm Topics : NumPy, NoSQL, ML, Recommenders CS 625 Data Visualization , Dr. Michele C. Weigle , Tuesdays, 9:30am-12:15pm Topics : Tableau, R, Vega-Lite, Data Types, Maps, Colors, Tables CS 891 Emerging Technologies , Dr. Justin F. Brunelle , Fridays, 3:00-5:30pm Topics : Cloud, Edge, IoT, Blockchain, Security, Agile CS 795/895 Intelligent User Interfaces , Dr. Vikas Ashok , Thursdays, 9:30am-12:15pm Topics : HCI, Assistive Technologies, Speech Interfaces, Wearable Computing, Crowd-Powered Interfaces I am on research leave for Fall 2019 and will n

The XKCD comic " Study " parodies  the challenges of recruiting study participants. As part of " Social Cards Probably Provide For Better Understanding Of Web Archive Collections " (recently accepted for publication by CIKM2019 ), I had to learn how to conduct user studies. One of the most challenging problems to solve while conducting user studies is recruiting participants. Amazon's Mechanical Turk (MT) solves this problem by providing a marketplace where participants can earn money by completing studies for researchers. This blog post summarizes the lessons I have learned from other studies that have successfully employed MT. I have found parts of this information scattered throughout different bodies of knowledge, but not gathered in one place; thus, I hope it is a useful starting place for future researchers. MT is by far the largest source of study participants, with over 100,000 available participants . MT is an automated system that facilitates

Tweet URL:  https://twitter.com/derekwillis/status/1127234631865118731 On May 11th 2019, Derek Willis , who works at Propublica and also maintains the Politwoops project, tweeted a list of deleted tweet ids found by Politwoops that could not be attributed to any Twitter handle being tracked by Politwoops. This was an opportunity for us to revisit our interest in  using web archives to uncover the deleted tweets . Although we were unsuccessful in finding  any of the deleted tweet ids  in web archives  provided by Politwoops, we are documenting our process for coming to this conclusion. Politwoops    Politwoops is a web service which tracks deleted tweets of elected public officials and candidates running for office in the USA and 55 other countries . The Politwoops USA is supported by  Propublica . Creating Twitter handles list for the 116th Congress  In a  previous post , we discussed the challenges involved in creating a data set of Twitter handles for

Figure 1: Screenshot from TweetedAt service showing timestamp for a  deleted tweet from @SpeakerPelosi Memento Link:  http://web.archive.org/web/20170905173455/https:/twitter.com/nancypelosi?lang=da On  May 11, 2019,  Derek Willis from Politwoops shared a list of deleted tweet IDs  which could not be attributed to any Twitter handle followed by them. We tried multiple techniques to find the list of deleted tweet IDs in the web archives, but we were unsuccessful in finding any of the tweet IDs in web archives within the time range of our analysis. During our  investigation, we learned of Snowflake, a service used to generate unique IDs by Twitter. We used Snowflake to extract the timestamp from the deleted tweet IDs. Of  the 107 deleted tweet IDs shared with us only seven of them were in the time range of our initial analysis . In this post, we describe TweetedAt , a web service and library to extract the timestamps for post-Snowflake IDs and estimate timestamps for pre