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

Title: Twitinfo: aggregating and visualizing microblogs for event exploration
Reference Information:
Adam Marcus, Michael Bernstein, Osama Badar, David Karger, Samuel Madden, and Robert Miller, "Twitinfo: aggregating and visualizing microblogs for event exploration". CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems. ACM New York, NY, USA. ©2011. ISBN: 978-1-4503-0228-9.
Author Bio:
Adam Marcus- I am a graduate student at the MIT Computer Science and Artificial Intelligence Lab (CSAIL), where I am part of the Database group and the Haystack group. My advisors are Sam Madden and David Karger.
Michael Bernstein- I am a final-year graduate student focusing on human-computer interaction at MIT in the Computer Science and Artificial Intelligence Lab. I work with Professors David Karger and Rob Miller.
Osama Badar- Graduate student at MIT in the CSAIL.
David Karger- I am a member of the Computer Science and Artificial Intelligence Laboratory in the EECS. department at MIT.
Samuel Madden- Sam is an Associate Professor in the EECS department at MIT. He is also a part of the CSAIL group.
Robert Miller- I'm an associate professor in the EECS department at MIT, and leader of the User Interface Design Group in the Computer Science and Artificial Intelligence Lab.
Summary:

• Hypothesis: If the authors can organize and effectively communicate the timeline-based display information of TwitInfo, then Twitter's existing implementation of information will be able to be more effectively manipulated and enhanced.
• Methods: The authors create an algorithm to automatically "browse an event" and label the created event (which was done using a keyword) if a certain event is tweeted about over a certain amount of times per time unit. This will allow the activity on a timeline to "peak" to show that users are tweeting about an event. The authors were also able to implement a "sentiment analysis" about certain events (i.e. is this event "positive" or "negative" judging by user's comments and feedback about the event). TwitInfo also allows the creation and browsing of subevents. TwitInfo was evaluated against three soccer matches as well as a month-long collection of raw data. The authors then recruited 12 people to evaluate the UI of TwitInfo.
• Results: TwitInfo is biased by the Twitter users' interests. It was effective in measuring events for the soccer matches as well as the earthquakes. Occassionally, TwitInfo returned false positives such as anyone tweeting about a soccer term that wasn't necessarily about the soccer match being observed. In the study of understanding the UI, the results concluded that TwitInfo is an effective source for news without even any prior knowledge of events. Users were able to find events on the timeline and read summaries of sub-events. Users didn't necessarily agree with the sentiment analysis, however.
• Content: The authors wanted a better way to browse Twitter events and effectively get caught up on events and relevant news for a keyword event. The created a UI which incorporated a timeline-based display where users could browse events with "high peaks" of interest and get caught up on them. The authors also created an engine to determine whether an event was a positive thing or a negative thing for each part of the world.

Discussions:
I think this is very interesting, but as an avid anti-Twitter-er, I won't be using this technology. I have nothing against Twitter, I just got a Facebook first. The way the authors were able to take all of the information from Twitter and personal tweets and organize it all into meaningful, timelined events that users could browse is a very good idea though. I believe the authors weren't compltely happy with their results as far as the user evaluation and journalist evaluation of TwitInfo went, but I'd say that they achieved their goals. The technology that they created is definitely better than what was already in-place for Twitter, anyway.