Knowledge Sharing in Online Discussion Threads: What Predicts the Ratings?
Knowledge Sharing in Online Discussion Threads: What Predicts the Ratings?
复制标题
在线讨论主题中的知识共享：什么可以预测收视率？
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Yuyang Liang
中科院分区：
文献类型：
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作者：
Yuyang Liang
问答社区作为用户生成内容（UGC）社区的一个重要类别，为互联网用户提供了提问和分享知识的机会。为了了解知识贡献质量的评级如何与知识在讨论线程中共享的方式相关，该研究考察了用户行为和配置文件在一个大型的知识共享社区，/r/Techsupport，一个基于讨论的Q&A网站在Reddit.com关于互联网和技术问题。通过建立负二项回归和负二项混合模型，研究了社区主题结构、用户活跃度、用户个人资料与社区主题和评论评分之间的关系。结果表明，在更好的评级线程，结构往往是更加集中的异构参与者讨论的问题在更深的层次。与此同时，具有良好评级的贡献更有可能由更多参与评论行为的用户产生。
As an important category of user-generated content (UGC) community, Question and Answer (Q&A) community offers internet users opportunities to ask questions and share knowledge with others. In order to understand how the ratings of knowledge contribution quality correlate with the way knowledge is being shared in discussion threads, the study examines user behaviors and profiles in a large knowledge sharing community, /r/Techsupport, a discussion based Q&A site in Reddit.com concerning internet and technology problems. Negative binomial regressions and negative binomial mixed models are built to investigate the relationships among thread structure, level of user activity, user profiles and the ratings of threads and comments in the community. Results indicate that in the better rated threads, the structures tend to be more centralized with heterogeneous participants discussing the problem at a deeper level. Meanwhile, contributions with good ratings are more likely to be produced by users who are more engaged in commenting behaviors.