Analyzing Payment-driven Targeted Q&A Systems

Today’s online question and answer (Q8A) services are receiving a large volume of questions. It becomes increasingly challenging 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” Q8A model where users ask questions to a target expert by paying the corresponding price. In this article, we perform a case study on two emerging targeted Q8A systems, Fenda (China) and Whale (U.S.), 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.