SIAIGTMAOct 7, 2023

User's Position-Dependent Strategies in Consumer-Generated Media with Monetary Rewards

arXiv:2310.04805v11 citationsh-index: 18
Originality Synthesis-oriented
AI Analysis

This research addresses the problem of designing effective incentive systems for CGM platform operators, though it appears incremental in applying existing models to new reward schemes.

The study tackled how monetary rewards affect user behavior and content quality in consumer-generated media, finding that reward schemes and network positions distinctly influence posting frequency and quality.

Numerous forms of consumer-generated media (CGM), such as social networking services (SNS), are widely used. Their success relies on users' voluntary participation, often driven by psychological rewards like recognition and connection from reactions by other users. Furthermore, a few CGM platforms offer monetary rewards to users, serving as incentives for sharing items such as articles, images, and videos. However, users have varying preferences for monetary and psychological rewards, and the impact of monetary rewards on user behaviors and the quality of the content they post remains unclear. Hence, we propose a model that integrates some monetary reward schemes into the SNS-norms game, which is an abstraction of CGM. Subsequently, we investigate the effect of each monetary reward scheme on individual agents (users), particularly in terms of their proactivity in posting items and their quality, depending on agents' positions in a CGM network. Our experimental results suggest that these factors distinctly affect the number of postings and their quality. We believe that our findings will help CGM platformers in designing better monetary reward schemes.

Foundations

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