基于新浪微博的2018年云南通海5.0级地震舆情时空特征分析
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摘要
基于新浪微博数据,通过数据清洗、分类和挖掘,分析2018年8月13,14日云南省通海县2次5. 0级地震舆情信息时空演变规律。研究结果表明:本次地震后微博活跃量总数激增,灾情类别主要集中于人的反应、器物反应方面,约占总数的79%;微博日活跃量与时间的关联度明显,震后出现多个波动;微博的数量和空间分布与距离震中远近密切相关,遵循距离衰减效应;地震灾区民众以负面情绪为主,由于地震期间谣传传播,恐慌、悲伤、疑惑情绪进一步加重。
Through cleansing,classification,and excavation the data of Sina Micro-blog,we analyzed the spatial and temporal characteristics of public opinion information of Tonghai M_S5. 0 earthquake on Aug. 13 and Aug. 14 in2018. The disaster information is divided into human reaction and utensil reaction,which accounts for more than79% of the amount. The correlation between the daily activity of micro-blog and time is obvious. After the earthquake,there are many fluctuations of the daily activity of micro-blog. The number and spatial distribution of micro-blog are closely related to the epicentral distance,and followthe distance attenuation effect. As rumors spread during the earthquake,people in the earthquake-stricken areas were mainly in negative mood,and the mood of panic,sadness and doubts increased.
Through cleansing,classification,and excavation the data of Sina Micro-blog,we analyzed the spatial and temporal characteristics of public opinion information of Tonghai M_S5. 0 earthquake on Aug. 13 and Aug. 14 in2018. The disaster information is divided into human reaction and utensil reaction,which accounts for more than79% of the amount. The correlation between the daily activity of micro-blog and time is obvious. After the earthquake,there are many fluctuations of the daily activity of micro-blog. The number and spatial distribution of micro-blog are closely related to the epicentral distance,and followthe distance attenuation effect. As rumors spread during the earthquake,people in the earthquake-stricken areas were mainly in negative mood,and the mood of panic,sadness and doubts increased.
引文
曹彦波,毛振江.2017a.基于微博数据挖掘的九寨沟7.0级地震灾情时空特征分析[J].中国地震,33(4):613-625.
曹彦波,吴艳梅,许瑞杰,等,2017b.基于微博舆情数据的震后有感范围提取研究[J].地震研究,40(2):303-310.
褚俊秀,徐敬海.2016.地震灾情位置微博抓取与展示[J].地理空间信息,14(5):38-40.
高永兵,陈超,熊振华,等.2016.基于个人微博特征的事件提取研究[J].计算机应用与软件,33(7):47-51.
胡悦,王亚民.2017.基于模糊神经网络的微博舆情趋势预测方法[J].情报科学,35(12):28-33.
雷程程,张岸,齐清文,等.2017.格网化的位置微博数据抓取与人群信息提取[J].测绘科学,42(2):125-129.
廉捷,周欣,曹伟,等.2011.新浪微博数据挖掘方案[J].清华大学学报(自然科学版),51(10):1300-1305.
刘国巍,程国辉,姜金贵.2015.时空分异视角下非常规突发事件网络舆情演化研究---以“上海12.31踩踏事件”为例[J].情报杂志,34(6):126-130.
全球媒体情报公司Cision.2017.2017全球社交新闻研究报告[EB/OL].(2017-10-09)[2018-08-20].http://www.useit.com.cn/thread-16735-1-1.html.
孙晓,叶嘉麒,唐陈意,等.2014.基于多策略的新浪微博大数据抓取及应用[J].合肥工业大学学报(自然科学版),37(10):1210-1215.
王昊,杨亮,林鸿飞.2012.日本地震的微博热点事件分析[J].中文信息学报,26(5):7-13.
王亚民,胡悦.2016.基于BTM的微博舆情热点发现[J].情报杂志,35(11):119-124,140.
徐敬海,褚俊秀,聂高众,等.2015.基于位置微博的地震灾情提取[J].自然灾害学报,24(5):12-18.
徐琳宏,林鸿飞,潘宇,等.2008.情感词汇本体的构造[J].情报学报,27(2):180-185.
赵金楼,成俊会.2015.基于SNA的突发事件微博舆情传播网络结构分析---以“4.20四川雅安地震”为例[J].电子商务与管理,27(1):148-157.
中共中央网络安全和信息化委员会办公室.2018.中国互联网络发展状况统计报告[EB/OL].(2018-01-31)[2018-08-20].http://www.cac.gov.cn/2018-01/31/c_1122347026.htm.
Cheng J W,Mitomo H,Otsuka T,et al.2016.Cultivation effects of mass and social media on perceptions and behavioural intentions in postdisaster recovery---The case of the 2011 Great East Japan Earthquake[J].Telematics and Informatics,33(3):753-772.
Li X,Wang Z,Gao C,et al.2017.Reasoning human emotional responses from large-scale social and public media[J].Applied Mathematics&Computation,310(C):182-193.
Seltzer E K,Jean N S,Golinkoff E K.2015.The content of social media's shared images about Ebola:a retrospective study[J].Public Health,129(9):1273-1277.
GB/T 17742-2008,中国地震烈度表[S].
GB/T 18207.1-2008,防震减灾术语第1部分:基本术语[S].
GB/T 18207.2-2005,防震减灾术语第2部分:专业术语[S].
曹彦波,吴艳梅,许瑞杰,等,2017b.基于微博舆情数据的震后有感范围提取研究[J].地震研究,40(2):303-310.
褚俊秀,徐敬海.2016.地震灾情位置微博抓取与展示[J].地理空间信息,14(5):38-40.
高永兵,陈超,熊振华,等.2016.基于个人微博特征的事件提取研究[J].计算机应用与软件,33(7):47-51.
胡悦,王亚民.2017.基于模糊神经网络的微博舆情趋势预测方法[J].情报科学,35(12):28-33.
雷程程,张岸,齐清文,等.2017.格网化的位置微博数据抓取与人群信息提取[J].测绘科学,42(2):125-129.
廉捷,周欣,曹伟,等.2011.新浪微博数据挖掘方案[J].清华大学学报(自然科学版),51(10):1300-1305.
刘国巍,程国辉,姜金贵.2015.时空分异视角下非常规突发事件网络舆情演化研究---以“上海12.31踩踏事件”为例[J].情报杂志,34(6):126-130.
全球媒体情报公司Cision.2017.2017全球社交新闻研究报告[EB/OL].(2017-10-09)[2018-08-20].http://www.useit.com.cn/thread-16735-1-1.html.
孙晓,叶嘉麒,唐陈意,等.2014.基于多策略的新浪微博大数据抓取及应用[J].合肥工业大学学报(自然科学版),37(10):1210-1215.
王昊,杨亮,林鸿飞.2012.日本地震的微博热点事件分析[J].中文信息学报,26(5):7-13.
王亚民,胡悦.2016.基于BTM的微博舆情热点发现[J].情报杂志,35(11):119-124,140.
徐敬海,褚俊秀,聂高众,等.2015.基于位置微博的地震灾情提取[J].自然灾害学报,24(5):12-18.
徐琳宏,林鸿飞,潘宇,等.2008.情感词汇本体的构造[J].情报学报,27(2):180-185.
赵金楼,成俊会.2015.基于SNA的突发事件微博舆情传播网络结构分析---以“4.20四川雅安地震”为例[J].电子商务与管理,27(1):148-157.
中共中央网络安全和信息化委员会办公室.2018.中国互联网络发展状况统计报告[EB/OL].(2018-01-31)[2018-08-20].http://www.cac.gov.cn/2018-01/31/c_1122347026.htm.
Cheng J W,Mitomo H,Otsuka T,et al.2016.Cultivation effects of mass and social media on perceptions and behavioural intentions in postdisaster recovery---The case of the 2011 Great East Japan Earthquake[J].Telematics and Informatics,33(3):753-772.
Li X,Wang Z,Gao C,et al.2017.Reasoning human emotional responses from large-scale social and public media[J].Applied Mathematics&Computation,310(C):182-193.
Seltzer E K,Jean N S,Golinkoff E K.2015.The content of social media's shared images about Ebola:a retrospective study[J].Public Health,129(9):1273-1277.
GB/T 17742-2008,中国地震烈度表[S].
GB/T 18207.1-2008,防震减灾术语第1部分:基本术语[S].
GB/T 18207.2-2005,防震减灾术语第2部分:专业术语[S].