多维度社交网络舆情用户群体聚类分析方法研究

Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network

【目的】通过舆情用户群体聚类为舆情监管部门和社交网络服务提供商定位用户群体特征、实施有针对性的管控措施提供新的视角和思路。【方法】以群体理论为基础,从用户的影响力特征、情感特征和行为特征出发进行聚类,通过采集新浪微博平台用户数据,利用Canopy、K-Means算法进行聚类,最终通过Neo4j和Weka进行可视化呈现。【结果】聚类结果表明,同一舆情事件的用户群体在情感、影响力和行为等方面存在差异,不同舆情事件的用户群体在上述方面也会存在相同点。【局限】两事件均为高校舆情事件,并且仅以新浪微博平台作为数据来源。【结论】根据聚类结果可针对相同舆情事件和不同舆情事件中的各个用户群体提出对应的管控策略。

[Objective]User groups are the main units to disseminate public opinion.This study identifies the characteristics of user groups through clustering techniques,which could help social network companies provide better services.[Methods]With the help of Group Theory,we clustered users based on their influence,sentiments,and behaviors.First,we collected user data from the Sina Weibo.Then,we utilized Canopy and K-Means algorithms to cluster users.Finally,we visualized our findings with Neo4j and Weka.[Results]User groups of the same public opinion event were different in emotion,influence,and behaviors,while user groups from different public opinion events shared common characteristics.[Limitations]Both public opinion events in this study happened at Chinese universities,and we only collected data from Sina Weibo.[Conclusions]Based on the clustering results,we could propose effective administration strategies for each user group in the same or different public opinion events.