Tweets as data: Demonstration of TweeQL and TwitInfo
Name
Karger_Tweets as data.pdf
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Author(s)
Marcus, Adam
Bernstein, Michael S.
Badar, Osama
Karger, David R.
Madden, Samuel R.
Miller, Robert C.
Date Issued
June 2011
Journal
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD '11)
Publisher
Association for Computing Machinery (ACM)
Citation
Adam Marcus, Michael S. Bernstein, Osama Badar, David R. Karger, Samuel Madden, and Robert C. Miller. 2011. Tweets as data: demonstration of TweeQL and Twitinfo. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD '11). ACM, New York, NY, USA, 1259-1262.
Version
Author's final manuscript
Abstract
Microblogs such as Twitter are a tremendous repository of user-generated content. Increasingly, we see tweets used as data sources for novel applications such as disaster mapping, brand sentiment analysis, and real-time visualizations. In each scenario, the workflow for processing tweets is ad-hoc, and a lot of unnecessary work goes into repeating common data processing patterns. We introduce TweeQL, a stream query processing language that presents a SQL-like query interface for unstructured tweets to generate structured data for downstream applications. We have built several tools on top of TweeQL, most notably TwitInfo, an event timeline generation and exploration interface that summarizes events as they are discussed on Twitter. Our demonstration will allow the audience to interact with both TweeQL and TwitInfo to convey the value of data embedded in tweets.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1145/1989323.1989470
