Pay-per-question: Towards targeted Q&A with payments

Steve T.K. Jan, Chun Wang, Qing Zhang, Gang Wang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

16 Citations (Scopus)


Online question and answer (Q&A) services are facing key challenges to motivate domain experts to provide quick and high-quality answers. Recent systems seek to engage real-world experts by allowing them to set a price on their answers. This leads to a “targeted” Q&A model where users to ask questions to a target expert by paying the price. In this paper, we perform a case study on two emerging targeted Q&A systems Fenda (China) and Whale (US) to understand how monetary incentives affect user behavior. By analyzing a large dataset of 220K questions (worth 1 million USD), we find that payments indeed enable quick answers from experts, but also drive certain users to game the system for profits. In addition, this model requires users (experts) to proactively adjust their price to make profits. People who are unwilling to lower their prices are likely to hurt their income and engagement over time.

Original languageEnglish
Title of host publicationGROUP 2018 - Proceedings of the 2018 ACM Conference on Supporting Groupwork
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Print)9781450355629
Publication statusPublished - 7 Jan 2018
Externally publishedYes
Event2018 ACM Conference on Supporting Groupwork, GROUP 2018 - Sanibel Island, United States
Duration: 7 Jan 201810 Jan 2018

Publication series

NameProceedings of the International ACM SIGGROUP Conference on Supporting Group Work


Conference2018 ACM Conference on Supporting Groupwork, GROUP 2018
Country/TerritoryUnited States
CitySanibel Island


  • Crowdsourcing
  • Online Q&A Service
  • Payments


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