“小众专家”特征识别——基于MetaFilter的实证分析

Feature Recognition of Niche Expert ——Empirical Analysis Based on Meta Filter Dataset

【目的】为全面获取专家资源,探究"小众专家"特征识别方法。【方法】以知名社交博客MetaFilter为例,利用用户社交活动数据,构建用户关系网络,统计节点网络结构指标:中介中心度和聚集系数。结合聚类分析和时序分析,判别不同时期节点特征及角色。【结果】综合类群网络特征,获取"小众专家"集合,依据集合时序变动情况细化"小众专家"分类。【局限】只对music版块评论关系进行角色判别及迁移分析,未来工作将扩展至更多版块,对比分析不同语义环境下"小众专家"类群"稳定–变化"特点。【结论】"小众专家"是对现有专家集合的有效补充,其识别研究可用于专家团队构建、专家推荐、专家检索等方面。

[Objective] In order to fully get expert resource, this paper explores the feature recognition method of niche expert. [Methods] Firstly, take advantage of user activity data from a famous community weblog named MetaFilter to construct user interaction network. Secondly, make statistics of node network structure indexes, such as betweenness centrality, clustering coefficient. Finally, feature and role of node in different period is distinguished via the combination of cluster analysis and time series analysis. [Results] This paper obtains the niche expert collection through comparative analysis of network statistics indexes of different clusters, the classification of niche experts are further refined based on temporal changes in the collection. [Limitations] Role identification and migration analysis should be expanded to more sections, not only the music section, so that the ＂stability-change＂ feature of niche experts under different semantic circumstance can be further discussed. [Conclusions] Niche expert is an effective supplement to the existing collection of experts, the method proposed in this paper can be applied to many aspects, such as the construction of expert team, the recommendations and retrieval of experts, and so on.