Multi-Feature Based Opinion Leader Mining in Social Networks

CAO Jiu-xin; CHEN Gao-jun; WU Jiang-lin; LIU Bo; ZHOU Tao; XU Shuai; ZHU Zi-qing

doi:10.3969/j.issn.0372-2112.2016.04.021

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Multi-Feature Based Opinion Leader Mining in Social Networks

更新时间：2025-07-16

- Multi-Feature Based Opinion Leader Mining in Social Networks
- Acta Electronica Sinica Vol. 44, Issue 4, Pages: 898-905(2016)
- 作者机构：
  
  1. 东南大学计算机科学与工程学院,江苏,南京,210096
  2. 东南大学计算机网络和信息集成教育部重点实验室,江苏,南京,210096
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.04.021
  CLC： TP393
- Published Online：25 April 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
CAO Jiu-xin, CHEN Gao-jun, WU Jiang-lin, et al. Multi-Feature Based Opinion Leader Mining in Social Networks[J]. Acta Electronica Sinica, 2016, 44(4): 898-905.
DOI：

CAO Jiu-xin, CHEN Gao-jun, WU Jiang-lin, et al. Multi-Feature Based Opinion Leader Mining in Social Networks[J]. Acta Electronica Sinica, 2016, 44(4): 898-905. DOI： 10.3969/j.issn.0372-2112.2016.04.021.

摘要

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.

Abstract

Mining opinion leaders in social network is important for analysis of information dissemination and evolution 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

according 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.

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