This paper was presented at CHI 2011.
Summary
Hypothesis
Researchers proposed that through Twitter timelines, events could be identified and visualized.
Methods
To complete this study, researchers used Twitter to search through various tweets about a given subject. The TwitInfo algorithm can begin to track certain keywords and when they're used together. It then begins to build a list of tweets that have those keywords mentioned. After these tweets are gathered together, the algorithm builds a list of peaks and determines what words are being used most often in the peaks. These peaks are then classified as events.
The system is also capable of detecting positive and negative sentiment over a given topic over time.
All of this data can be then be organized and displayed in order to better visualize results.
In order to test the user interface of TwitInfo, researchers recruited 12 participants to perform certain tasks to locate an event.
Results
Tested over time, TwitInfo correctly organized most events properly. It was used frequently to analyze the events of a given soccer match. For every match, it's easy to see when the goals were scored due to the peaks in the events.
In the participant study of TwitInfo, participants were quick to reconstruct events when given information from TwitInfo, in many cases, in less than 5 minutes.
Discussion
TwitInfo struck me initially as a very useful, concrete research project. It is a classic example of using humans to complete a given task and then aggregate the data. Essentially, humans, without even knowing it are classifying events and when they occur. This makes TwitInfo's job easier. All the algorithm has to do is collect and analyze the data.
No comments:
Post a Comment