基于用户细分的社会化问答社区知识贡献激励机制研究

    Research on Knowledge Contribution Incentive Mechanism in Social Q&A Community based on User Segmentation

    • 摘要: 目前社会化问答社区用户知识贡献日渐呈现出“90-9-1”金字塔结构,节点中心度的增加会对长尾用户的流量扶持及曝光产生直接影响。如何提高长尾用户的知识贡献意愿、孵化培养出核心用户并鼓励其传播优质付费知识对社区发展至关重要。基于社会化问答社区用户群体细分视角,通过系统动力学剖析细分群体内部的知识贡献、声誉报酬及群体转换系数间的反馈回路,绘制因果关系图与流量存量图,结合专家打分估计模型参数,探讨各类激励因素对细分群体转换及知识贡献产生的影响。研究结果表明:不同细分群体均呈现出高速增长态势;相较于核心用户的缓急增长,长尾用户表现出更为平稳的边际递增效应;腰部用户在经历初期免费模式认知锁定后,其付费知识贡献呈现稳定增长态势。

       

      Abstract: At present, user knowledge contribution of social Q&A community is gradually showing a “90-9-1” pyramid structure, and the centrality of nodes will increase It has a direct impact on the traffic support and exposure of long-tail users; how to improve the knowledge contribution of long-tail users, incubate and cultivate core users, and encourage them to spread high-quality paid knowledge is very important for community development. This paper analyzes the feedback loop between the knowledge contribution, reputation reward and group conversion coefficient within the segmented group through system dynamics, draw the causality diagram and the flow stock diagram, combine the expert scores to estimate the model parameters, explore various motivational factors and the impact on the conversion of sub-groups and the contribution of knowledge. The results show that the sub-groups all show a rapid growth trend. Compared with the rapid growth of core users, long-tail users show a more stable marginal increasing effect. After the waist users experience the initial cognitive lock-in of the free model, their paid knowledge contribution will show a steady growth trend.

       

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