基于多维特征分析的社交网络意见领袖挖掘

在社交网络中进行意见领袖的挖掘对信息传播与演化的深度分析、舆情监控和引导具有重要意义,本文综合结构特征、行为特征和用户的情感特征对意见领袖节点挖掘问题进行研究.本文首先对微博真实文本数据进行话题识别得到主题社区,在主题社区中基于用户节点之间的关注关系构建交互网络拓扑.然后分别从结构、行为和情感三个维度对用户的影响力进行度量.最后,分析用户在主题社区中的影响力分布与传播规律,提出意见领袖识别算法MFP(Multi-Feature PageRank).实验表明,该算法可有效地挖掘潜在的意见领袖节点,能够获得较高的支持率.

Mu lti-Featu re Based Opinion Leader Mining in Social Networks

Mining opinion leaders in social network is important for analysis of information dissemination and evolu-tion of public opinion.This paper conducts the study on this problem considering structural features,behavior and emotional characteristics comprehensively.Firstly,we extract topics from micro-blogging texts,and get user communities according to the topic division,and an interactive network topology of topic community is built with the following relationships.Then, three kinds of user feature are gained from different aspect:network structure,user behavior and user sentiment.Finally,ac-cording to the analysis of users’influence distribution,opinion leaders mining algorithm MFP (Multi-Feature PageRank)is proposed.Experiments show that the algorithm can obtain the potential opinion leader nodes effectively,and have a good performance in support rate from other user nodes.