Database Paper Browser

Back to papers

Minimizing Efforts in Validating Crowd Answers

Summary: Proposes a probabilistic model to identify the most beneficial validation questions in crowdsourced answers, enhancing correctness and flagging faulty workers. Demonstrates up to 50% expert savings and near-perfect correctness after only ~20% of questions are validated. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4951
Venue
SIGMOD
Year
2015
Pagerank
4.5366717e-05
Overall Rank
8,362 | 41.83%
DOI
10.1145/2723372.2723731

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
3,773 Cleaning Crowdsourced Labels Using Oracles for Statistical Classification 2019 VLDB 6.7758649e-05
7,648 User Guidance for Efficient Fact Checking 2019 VLDB 4.6889787e-05
11,770 Staging User Feedback toward Rapid Conflict Resolution in Data Fusion 2017 SIGMOD 4.1945683e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers