Paper Reading #25: Twitinfo: aggregating and visualizing microblogs for event exploration

References
Twitinfo: aggregating and visualizing microblogs for event exploration by 

Adam Marcus, Michael S. Bernstein, Osama Badar, David R. Karger, Samuel Madden, Robert C. Miller.  Published in the CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems.

Author Bios

• Adam Marcus
• Michael S. Bernstein is a graduate student focusing on human-computer interaction at MIT in the CSAIL.  His research is on crowd-powered interfaces: interactive systems that embed human knowledge and activity.
• Osama Badar is currently a member of the CSAIL at MIT.
• David R. Karger is a member of the CSAIL in the EECS department at MIT.  He is interested in information retrieval and analysis of algorithms. 
• Samuel Madden is currently an associate professor in the EECS department at MIT.  His primary research is in database systems.
• Robert C. Miller is an associate professor in the EECS department at MIT and leads the User Interface Design Group.  His research interests include web automation and customization, automated text editing, end-user programming, usable security, and other issues in HCI.

Summary

• Hypothesis
• TwitInfo can provide a useful tool for summarizing and searching twitter for information about events and trends.
• Methods 
• The researchers asked 12 participants to use TwitInfo to research different aspects of a recent event.  During this part of the process they gathered usability feedback and observed which interface objects were useful or ignored.  The second part of the testing involved adding a time limit.  Participants were given 5 minutes to research the event using Twitinfo and then 5 minutes to compose a report about their findings.  At the end of the session, the participants were interviewed about their reactions to the Twitinfo system.
• Results
• They found that participants were able to reconstruct reasonably detailed information about events even without prior knowledge of it.  They found that when users were performing the freeform exploration they tended to explore the largest peak thoroughly and read tweets completely.  They also drilled in on the map and followed links to related articles.  Most of the tweets were only used to confirm event details rather than generate the information.  When the time constraint was introduced, the focus shifted to skimming peak labels for a broad sense of the event a chronology, and a few people honed in on only one or two links to outside news sources to minimize time spent searching through repeated information.
• Contents
• This article mainly spends time explaining how Twitinfo works and the details that went into its creation.  It describes the user testing and results, and identifies several key trends in how people use the tool.  

Discussion
This article was not particularly convincing to me; while the authors successfully created their product, it didn't seem as useful as they'd thought it might be.  Frankly, I can't imagine it being used other than as a way to garner general public opinion on events because it is simply much easier to search for related news articles.  It might potentially be useful in a smaller group setting, perhaps if a high school student wanted to read some first-hand posts about a fight that went down after lunch or something.