基于地理标签的推文话题时空演变的可视分析方法

doi:10.11896/j.issn.1002-137X.2019.08.007

摘要/Abstract

摘要： 社交媒体中,用户所发布的推文内容记录了与用户相关的各种信息。文字信息中涵盖了推文中包含的各种话题,以及时间和空间信息,从这些信息中分析出话题的时空演变情况具有十分重要的研究意义。针对推文数据,设计了一套可视分析流程来挖掘推文信息,通过用户交互的方式多角度地展示了推文话题的时空演变过程。首先,基于部分历史推文数据,通过DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法,结合泰森多边形对全球地理空间进行区域划分;然后,针对用户查询搜索的兴趣话题,索引找到所有相关的推文内容,并将信息与聚类中心绑定;最后,通过设计的多个结合时序聚类算法和自适应算法的可视化视图来展示话题的时空演变过程。通过推特官网提供的API抓取存储的推文数据,并进行实验和分析,结果表明:改进的可视化视图自适应布局算法有效地解决了图形遮挡问题,完整展现了推文的时空演变模式;地理区域的划分以及可视化组件能够有效帮助研究人员分析推文的时空演变以及全球关注的热点话题分布。

关键词: 聚类, 可视化分析流程, 时空演变, 推文话题, 自适应布局算法

Abstract: The tweets posted by users in social media record a wide variety of user information.The text information includes various topics contained in the tweet.It is very important to analyze the temporal and spatial evolution of topics from these messages.Based on the tweet data,this paper designed a set of visual analysis process to mine the tweet information and display the spatiotemporal evolution process of the tweet topic through user interaction.Specifically,based on the partial historical tweet data,the global geographic space is divided by the DBSCAN clustering algorithm combined with the Tyson polygon.For the user to query the search topic of interest,the index finds all relevant tweet content and binds the information to the cluster center.Finally,the temporal and spatial evolution of the topic is demonstrated by the design of multiple combined time series clustering algorithms and visualization components of the adaptive algorithm.Through the experiment and analysis of the tweet data stored in the API provided by Twitter official website,the improved visual view adaptive layout algorithm effectively solves the problem of graphic occlusion and fully displays the temporal and spatial evolution mode of the tweet.The division of geographic regions and visualization components can effectively help researchers analyze the temporal and spatial evolution of tweets,as well as the distribution of hot topics of global concern

Key words: Adaptive layout algorithm, Clustering, Spatio-Temporal evolution, Tweet topic, Visual analysis process

中图分类号:

TP391

孙国道, 周志秀, 李思, 刘义鹏, 梁荣华. 基于地理标签的推文话题时空演变的可视分析方法[J]. 计算机科学, 2019, 46(8): 42-49. https://doi.org/10.11896/j.issn.1002-137X.2019.08.007

SUN Guo-dao, ZHOU Zhi-xiu, LI Si, LIU Yi-peng, LIANG Rong-hua. Spatio-Temporal Evolution of Geographical Topics[J]. Computer Science, 2019, 46(8): 42-49. https://doi.org/10.11896/j.issn.1002-137X.2019.08.007

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