Paper Reading #25

Twitinfo: aggregating and visualizing microblogs for event exploration

 

Authors:	Adam Marcus	Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
	Michael S. Bernstein	Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
	Osama Badar	Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
	David R. Karger	Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
	Samuel Madden	Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
	Robert C. Miller	Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

 

Proceeding  

CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems 

 

Summary

• Hypothesis - The Researchers believe that events in twitter can be determined using timelines from the Twitter API and algorithms in a system called TwitInfo.

• Methods/Content - TwitInfo takes a keyword and gathers tweets about the keyword for a period of time. After collection, the tweets are organized by the internal algorithms. From there, peaks are calculated and classified into events. The system can also determine whether the sentiment of individual tweets are positive or negative using machine learning. To test the system, 12 participants were asked to use it to find events on certain subjects.
• Results - Most events were found properly using the system. For soccer games, there were peaks during the individual goals, making it easier to visualize the information. The participants were able to recreate the details of an event usually within 5 minutes.

Discussion
There is an enormous amount of data on Twitter considering so little text is in each individual tweet. What makes Twitter such a good medium for communicating data is the fact that information is shared directly when an event happens. A strange example of this is when a person in Pakistan tweeted about hearing the (stealth) helicopters used to kill Osama bin Laden. Using TwitInfo to analyze this event would be very interesting. I'm imagining this system could be used by politicians to determine how well their constituency agrees with their policies, since the System already calculates positive or negative emotions.