Processing and visualizing the data in tweets

• Main Authors: Marcus, Adam (Contributor), Bernstein, Michael S. (Author), Badar, Osama (Contributor), Karger, David R. (Contributor), Madden, Samuel R. (Contributor), Miller, Robert C. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Michael S. Bernstein (Contributor)
• Format: Article
• Language: English
• Published: Association for Computing Machinery, 2013-06-20T15:03:28Z.
• Subjects: Article
• Online Access: Get fulltext

 Microblogs such as Twitter provide a valuable stream of diverse user-generated data. While the data extracted from Twitter is generally timely and accurate, the process by which developers extract structured data from the tweet stream is ad-hoc and requires reimplementation of common data manipulation primitives. In this paper, we present two systems for querying and extracting structure from Twitter-embedded data. The first, TweeQL, provides a streaming SQL-like interface to the Twitter API, making common tweet processing tasks simpler. The second, TwitInfo, shows how end-users can interact with and understand aggregated data from the tweet stream, in addition to showcasing the power of the TweeQL language. Together these systems show the richness of content that can be extracted from Twitter.