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Stimulating high quality social media through knowledge barter-auctioning
Ji, Qijin Shen, Haifeng Mao, Yuqing Zhu, Yanqing
Ji, Qijin
Shen, Haifeng
Mao, Yuqing
Zhu, Yanqing
Abstract
Incentives play a pivotal role in stimulating user-generated content (UGC), which is critical to the viability and success of today's social computing services. Non-financial social incentives are generally effective in boosting the quantity, but have limited effect on the quality. Conversely, financial incentives generally motivate better quality, but often complicate the efforts to attract quantity. In this paper, we propose knowledge barter-auctioning, a non-financial remunerative mechanism that is particularly effective in stimulating the quality of UGC yet without detriment to its quantity. This mechanism provides an optimal way for the knowledge vendor to choose the best barter partner in order to maximise their expected revenue, which is an extrinsic motivation for the triumph of quality as UGC of higher quality will enable the vendor to attract more bidders and consequently make a higher revenue through the barter auction. We have conducted a series of experiments using a real-world dataset to analyse the ramifications of UGC quality in knowledge bartering processes.
Keywords
user-generated content, incentive, bartering, optimal auction
Date
2014
Type
Conference item
Journal
Book
SocialCom '14: Proceedings of the 2014 International Conference on Social Computing
Volume
Issue
Page Range
4-11
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
Open access
License
File Access
Open