Naeemul Hassan
Abstract
Towards computational journalism, we present FactWatcher, a system that helps journalists identify data-backed, attention-seizing facts which serve as leads to news stories. FactWatcher discovers three types of facts, including situational facts, one-of-the-few facts, and prominent streaks, through a unified suite of data model, algorithm framework, and fact ranking measure. Given an append-only database, upon the arrival of a new tuple, FactWatcher monitors if the tuple triggers any new facts. Its algorithms efficiently search for facts without exhaustively testing all possible ones. Furthermore, FactWatcher provides multiple features in striving for an end-to-end system, including fact ranking, fact-to-statement translation and keyword-based fact search.
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Digital Library
- S. Cohen, C. Li, J. Yang, and C. Yu. Computational journalism: A call to arms to database researchers. In CIDR, pages 148--151, 2011.Google Scholar
- X. Jiang, C. Li, P. Luo, M. Wang, and Y. Yu. Prominent streak discovery in sequence data. In KDD, pages 1280--1288, 2011. Google ScholarDigital Library
- A. Sultana, N. Hassan, C. Li, J. Yang, and C. Yu. Incremental discovery of prominent situational facts. In ICDE, pages 112--123, 2014.Google ScholarCross Ref
- Y. Wu, P. K. Agarwal, C. Li, J. Yang, and C. Yu. On one of the few objects. In KDD, pages 1487--1495, 2012. Google ScholarDigital Library
- G. Zhang, X. Jiang, P. Luo, M. Wang, and C. Li. Discovering general prominent streaks in sequence data. ACM TKDD, 8(2): 9:1--9:37, June 2014. Google ScholarDigital Library
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