基于关键词信息的微博用户行为度量分析研究

发布时间：2018-05-30 20:24

本文选题：微博 + 用户行为　；参考：《科技通报》2017年05期

【摘要】：近年来,微博用户行为分析与挖掘逐步成为社交网络领域研究热点,本文基于微博传播机制及用户特点,提出了一种基于高频关键词信息的微博内容筛选算法,结合好友联合影响概率确定关键微博用户,并分析其影响力及具体范围。实验数据分析表明:本文设计的算法是可行的,与传统方法相比,本文算法在高频关键词提取和微博用户行为分析方面,能够有效提高话题传播中微博用户影响力度量的准确性。
[Abstract]:In recent years, Weibo user behavior analysis and mining has gradually become a hot topic in the field of social networks. Based on the Weibo propagation mechanism and user characteristics, a Weibo content filtering algorithm based on high-frequency keyword information is proposed in this paper. The key Weibo users are determined with the joint impact probability of friends, and their influence and specific scope are analyzed. Experimental data analysis shows that the algorithm designed in this paper is feasible. Compared with the traditional methods, the algorithm in this paper is used in high frequency keyword extraction and Weibo user behavior analysis. It can effectively improve the accuracy of Weibo user impact measurement in topic communication.
【作者单位】：浙江警察学院计算机与信息技术系;浙江警察学院实验中心;
【基金】：国家自然科学基金重点项目(U1509219 浙江省教育厅科研项目(Y201224395) 浙江警察学院校级科研项目(20140622)
【分类号】：TP393.092

【参考文献】