iCheck: Computationally Combating "Lies, D-ned Lies, and Statistics"
Summary: iCheck models claims on structured data as parameterized queries and analyzes how perturbing parameters change results to gauge claim quality beyond accuracy. It counters cherry-picked evidence by measuring robustness, demonstrated on congress voting records, sports statistics, and publication records of database researchers. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. You Wu
- 2. Brett Walenz
- 3. Peggy Li
- 4. Andrew Shim
- 5. Emre Sonmez
- 6. Pankaj K. Agarwal
- 7. Chengkai Li
- 8. Jun Yang
- 9. Cong Yu
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,972 | Verifying Text Summaries of Relational Data Sets | 2019 | SIGMOD | 5.7931494e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,340 | Toward Computational Fact-Checking | 2014 | VLDB | 7.2030091e-05 |
| 4,157 | Computational Journalism: A Call to Arms to Database Researchers | 2011 | CIDR | 6.3997218e-05 |
| 4,299 | Fact Checking and Analyzing the Web | 2013 | SIGMOD | 6.2885419e-05 |
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