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New Technology of Library and Information Service  2016, Vol. 32 Issue (2): 90-101    DOI: 10.11925/infotech.1003-3513.2016.02.12
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Analyzing Geographical Coordinates Data for Micro-blog Trending Events
Li Jinhua(),An Zhongjie
School of Information Management, Central China Normal University, Wuhan 430079, China
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[Objective] This study aims to retrieve the trending events from the micro-blog platform with the help of data mining algorithms. [Methods] First, we collected micro-blog message with geographic coordinates from the most popular platform (the Sina Weibo) using its API service. Then, we used the K-means, KNN and decision trees algorithms to construct the geographical patterns of those collected posts. The number of published posts, re-tweets, and comments, as well as user activity and movement strength were also examined. Third, we compared these geographical patterns with the daily regional micro-blog data to identify breaking news in that area. [Results] We analyzed data collected on April 15 and April 16 of 2015 with the help of the proposed model, and found a trending event of “Beijing Sandstorm”. [Limitations] The sample size was small, which might influence the results. [Conclusions] Geographic coordinates could help us detect trending events on the Sina Weibo, and this new method will also support the government’s crisis management strategy and decision-making process.

Key wordsMicro-blog      Event detection      Visualization analysis      Geographical coordinates analysis     
Received: 24 September 2015      Published: 08 March 2016

Cite this article:

Li Jinhua,An Zhongjie. Analyzing Geographical Coordinates Data for Micro-blog Trending Events. New Technology of Library and Information Service, 2016, 32(2): 90-101.

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[1] 胡吉明. 社会化网络服务的开放运行架构及服务拓展研究[J]. 情报科学, 2012, 30(9): 1396-1400.
[1] (Hu Jiming.Study on Open Operation Architecture and Service Expansion of Social Network Service[J]. Information Science, 2012, 30(9): 1396-1400.)
[2] 李彪. 微博意见领袖群体“肖像素描”——以40个微博事件中的意见领袖为例[J]. 新闻记者, 2012(9):19-25.
[2] (Li Biao.The “Portrait Sketch” of Microblogging Opinion Leaders Group——Take 40 Opinion Leaders from Microblogs as an Example[J]. Journalism Review, 2012(09): 19-25.)
[3] 杨亮, 林原, 林鸿飞. 基于情感分布的微博热点事件发现[J]. 中文信息学报, 2012, 26(1):84-90.
[3] (Yang Liang, Lin Yuan, Lin Hongfei.Micro-Blog Hot Events Detection Based on Emotion Distribution[J]. Journal of Chinese Information Processing, 2012, 26(1): 84-90.)
[4] 王林, 时勘, 赵杨, 等. 基于突发事件的微博集群行为舆情感知实验[J]. 情报杂志, 2013, 32(5): 32-37.
[4] (Wang Lin, Shi Kan, Zhao Yang, et al.Experimental Studies on Public Opinion Perception of the Micro Blog’s Collective Behavior Based on the Emergencies[J]. Journal of Intelligence, 2013, 32(5): 32-37.)
[5] 杨娟娟, 杨兰蓉, 曾润喜, 等. 公共安全事件中政务微博网络舆情传播规律研究——基于“上海发布”的实证[J]. 情报杂志, 2013, 32(9):11-15.
[5] (Yang Juanjuan, Yang Lanrong, Zeng Runxi, et al.Research on Communication Mechanism of Internet Public Opinion of Government Affairs Microblog in Public Security Events: A Case Study of the “Shanghai Fabu”[J]. Journal of Intelligence, 2013, 32(9): 11-15.)
[6] 兰月新. 突发事件微博舆情扩散规律模型研究[J]. 情报科学, 2013, 31(3): 31-34.
[6] (Lan Yuexin.Research on Microblog Opinion Diffusion Model of Emergent Events[J]. Information Science, 2013, 31(3): 31-34.)
[7] 王勇, 肖诗斌, 郭跇秀, 等. 中文微博突发事件检测研究[J]. 现代图书情报技术, 2013(2): 57-62.
[7] (Wang Yong, Xiao Shibin, Guo Yixiu, et al.Research on Chinese Micro-blog Bursty Topics Detection[J]. New Technology of Library and Information Service, 2013(2): 57-62.)
[8] 陈国兰. 基于爆发词识别的微博突发事件监测方法研究[J]. 情报杂志, 2014, 33(9): 123-128.
[8] (Chen Guolan.Micro-blog Emergencies Detection Approach Based on Burst Words Distinguishing[J]. Journal of Intelligence, 2014, 33(9): 123-128.)
[9] 刘志明, 刘鲁. 微博网络舆情中的意见领袖识别及分析[J]. 系统工程, 2011, 29(6): 8-16.
[9] (Liu Zhiming, Liu Lu.Recognition and Analysis of Opinion Leaders in Microblog Public Opinions[J]. Systems Engineering, 2011, 29(6): 8-16.)
[10] 魏志惠, 何跃. 基于信息熵和未确知测度模型的微博意见领袖识别——以“甘肃庆阳校车突发事件”为例[J]. 情报科学, 2014, 32(10): 38-43.
[10] (Wei Zhihui, He Yue.Identify Microblogging Opinion Leaders Based on Information Entropy and Unascertained Measure Model——Taking “Emergencies of Qingyang School Bus” as an Example[J]. Information Science, 2014, 32(10): 38-43.)
[11] 田野. 基于微博平台的事件趋势分析及预测研究[D]. 武汉: 武汉大学, 2012.
[11] (Tian Ye.On Trends Analysis and Prediction Based on Micro-Blogging Platforms [D]. Wuhan: Wuhan University, 2012.)
[12] Yang Y, Carbonell J, Brown R.Multi-Strategy Learning for Topic Detection and Tracking [A]. // Topic Detection and Tracking[M]. Springer, 2002: 85-114.
[13] 冯永, 韩楠, 贾东风. 云计算环境下基于代表点增量层次密度聚类的微博事件检测及跟踪[J]. 计算机应用, 2013, 33(12): 3559-3562.
[13] (Feng Yong, Han Nan, Jia Dongfeng.Microblog Events Detection and Tracking with Incremental Hierarchical DBSCAN Based on Representative Posts Using Cloud Framework[J]. Journal of Computer Applications, 2013, 33(12): 3559-3562.)
[14] 王连喜. 微博短文本预处理及学习研究综述[J]. 图书情报工作, 2013, 57(11):125-131.
[14] (Wang Lianxi.A Literature Review on Pre-processing and Learning of Microtext[J]. Library and Information Service, 2013, 57(11): 125-131.)
[15] Fu C, Samet H, Sankaranarayanan J.WeiboStand: Capturing Chinese Breaking News Using Weibo “Tweets” [C]. In: Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-Based Social Networks. 2014.
[16] 王锋. 灾难性事件中的“微”力量——青海玉树地震中微博应用探析[J]. 新闻世界, 2010(S2): 149-150.
[16] (Wang Feng.“Micro” Forces of the Catastrophic Event——Qinghai Yushu Weibo Application Analysis in the Earthquake[J]. News World, 2010(S2): 149-150.)
[17] Zhang P.Social Inclusion or Exclusion? When Weibo (Microblogging) Meets the “New Generation” of Rural Migrant Workers[J]. Library Trends, 2013, 62(1):63-80.
[18] 微博数据中心. 2014年微博用户发展报告[R/OL]. [2015- 02-06]. .
[18] (Weibo Data Center. The 2014 Report of Weibo Users Development [R/OL]. [2015-02-06].
[19] 吴夙慧, 成颖, 郑彦宁, 等. K-means算法研究综述[J]. 现代图书情报技术, 2011(5): 28-35.
[19] (Wu Suhui, Cheng Ying, Zheng Yanning, et al.Survey on K-means Algorithm[J]. New Technology of Library and Information Service, 2011(5): 28-35.)
[20] 亓峰, 刘昆, 张超, 等. 圆和维诺图相交模拟基站覆盖算法[J]. 北京邮电大学学报, 2014, 37(S1): 108-114.
[20] (Qi Feng, Liu Kun, Zhang Chao, et al.A Novel Base Station Coverage Simulation Based on Intersection of Circle and Voronoi[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(S1): 108-114.)
[21] 江涛, 陈小莉, 张玉芳, 等. 基于聚类算法的KNN文本分类算法研究[J]. 计算机工程与应用, 2009, 45(7): 153-158.
[21] (Jiang Tao, Chen Xiaoli, Zhang Yufang, et al.Improved KNN Using Clustering Algorithm[J]. Computer Engineering and Applications, 2009, 45(7): 153-158.)
[22] 陆安生, 陈永强, 屠浩文. 决策树C5算法的分析与应用[J]. 电脑知识与技术, 2005(3): 17-20.
[22] (Lu Ansheng, Chen Yongqiang, Tu Haowen.The Analysis and Application of Decision Tree Algorithm of C5[J]. Computer Knowledge and Technology, 2005(3): 17-20.)
[23] 迟呈英, 李红. 基于改进TF*PDF算法的网络新闻热点话题检测和跟踪[J]. 计算机应用与软件, 2013, 30(12): 311-314.
[23] (Chi Chengying, Li Hong.Network News Hot Topics Detection and Tracking Based on Modified TF*PDF Algorithm[J]. Computer Applications and Software, 2013, 30(12): 311-314.)
[24] 谢科范, 赵湜, 陈刚, 等. 网络舆情突发事件的生命周期原理及集群决策研究[J]. 武汉理工大学学报: 社会科学版, 2010, 23(4): 482-486.
[24] (Xie Kefan, Zhao Shi, Chen Gang, et al.Research on Lifecycle Principle and Group Decision- making of Network Public Sentiment Emergency[J]. Journal of Wuhan University of Technology: Social Sciences Edition, 2010, 23(4): 482-486.)
[25] Narayanam R, Narahari Y.A Shapley Value-based Approach to Discover Influential Nodes in Social Networks[J]. IEEE Transactions on Automation Science and Engineering, 2011, 8(1): 130-147.
[26] 陈吉荣, 乐嘉锦. 基于Hadoop生态系统的大数据解决方案综述[J]. 计算机工程与科学, 2013, 35(10): 25-35.
[26] (Chen Jirong, Le Jiajin.Reviewing the Big Data Solution Based on Hadoop Ecosystem[J]. Computer Engineering & Science, 2013, 35(10): 25-35.)
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