<commit_before>---
title: "Drafty: Enlisting Users to be Editors who Maintain Structured Data"
collection: publications
permalink: /publication/2017-Drafty
excerpt: 'Drafty is a platform that enlists visitorsof an editable dataset to become “user-editors” to help solvethis  problem.  It  records  and  analyzes  user-editors’  within-page interactions to construct user interest profiles, creatinga cyclical feedback mechanism that enables Drafty to targetrequests  for  specific  corrections  from  user-editors.  To  val-idate  the  automatically  generated  user  interest  profiles,  wesurveyed participants who performed self-created tasks withDrafty and found their user interest score was 3.2 higher ondata they were interested in versus data they had no interestin.  Next,  a  7-month  live  experiment  compared  the  efficacyof  user-editor  corrections  depending  on  whether  they  wereasked to review data that matched their interests. Our find-ings suggest that user-editors are approximately 3 times morelikely to provide accurate corrections for data matching theirinterest profiles, and about 2 times more likely to provide cor-rections in the first place.'
date: 2017
venue: 'AAAI Conference on Human Computation and Crowdsourcing'
paperurl: 'https://jeffhuang.com/Final_Drafty_HCOMP17.pdf'
---

[Download paper here](https://jeffhuang.com/Final_Drafty_HCOMP17.pdf)

<!---Recommended citation: Your Name, You. (2009). "Paper Title Number 1." <i>Journal 1</i>. 1(1)<commit_msg>Update 2017-HCOMP.md<commit_after>---
title: "Drafty: Enlisting Users to be Editors who Maintain Structured Data"
collection: publications
permalink: /publication/2017-HCOMP
excerpt: 'Drafty is a platform that enlists visitorsof an editable dataset to become “user-editors” to help solvethis  problem.  It  records  and  analyzes  user-editors’  within-page interactions to construct user interest profiles, creatinga cyclical feedback mechanism that enables Drafty to targetrequests  for  specific  corrections  from  user-editors.  To  val-idate  the  automatically  generated  user  interest  profiles,  wesurveyed participants who performed self-created tasks withDrafty and found their user interest score was 3.2 higher ondata they were interested in versus data they had no interestin.  Next,  a  7-month  live  experiment  compared  the  efficacyof  user-editor  corrections  depending  on  whether  they  wereasked to review data that matched their interests. Our find-ings suggest that user-editors are approximately 3 times morelikely to provide accurate corrections for data matching theirinterest profiles, and about 2 times more likely to provide cor-rections in the first place.'
date: 2017
venue: 'AAAI Conference on Human Computation and Crowdsourcing'
paperurl: 'https://jeffhuang.com/Final_Drafty_HCOMP17.pdf'
---

[Download paper here](https://jeffhuang.com/Final_Drafty_HCOMP17.pdf)

<!---Recommended citation: Your Name, You. (2009). "Paper Title Number 1." <i>Journal 1</i>. 1(1)
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