基于SNA的微博舆情传播核心节点分析研究

Based on SNA microblogging public opinion transmission core node analysis

负面网络舆情对社会稳定具有危害性,因此为了引导舆情良性发展,快速找到网络舆情传播中的核心节点是非常重要的。文中以社会网络分析为基础,通过对微博信息和微博用户网络结构进行分析,提出一种新颖的网络成员重要度评价算法。给出了节点粉丝影响度的概念,并对节点粉丝影响度中心度算法进行了改进。实验分析显示,文中算法排序精度在整体上高于SNA算法,因而对准确预测微博舆情传播中的核心节点具有很好的参考价值。

Negative public sentiment is harmful to social stability, so it is important to quickly find core nodes in public sentiment communication network for guiding the benign development of public sentiment. In this paper, a novel algorithm for evaluating the significance of network members is proposed by analyzing micro-blogs and the user structure residing in them based on social network analysis（SNA）. The concept of fans＇ influence of a node is defined, and the node centrality algorithm is also improved for fans＇ influence. The experiments show that the algorithm proposed in this paper overall has more precise sorting than the traditional SNA algorithm, and therefore has a promising potential for predicting core members in public sentiment communication network.