Tweets as data: demonstration of TweeQL and Twitinfo

Authors:
Adam Marcus;Michael S. Bernstein;Osama Badar;David R. Karger;Samuel Madden;Robert C. Miller
Affiliations:
MIT CSAIL, Cambridge, MA, USA;MIT CSAIL, Cambridge, MA, USA;MIT CSAIL, Cambridge, MA, USA;MIT CSAIL, Cambridge, MA, USA;MIT CSAIL, Cambridge, MA, USA;rcm@csail.mit.edu, Cambridge, MA, USA
Venue:
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Year:
2011

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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.