Pay-per-Question: Towards Targeted Q&A with Payments
Association for Computing Machinery
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.