基于社交媒体的地震灾区民众情绪反应分析
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摘要
社会感知技术是研究重特大地震事件中灾区民众行为反应时空特征的一种有效手段。采用情感词典和规则相结合的方法,以2013年四川芦山7.0级和2017年九寨沟7.0级地震为例,用震后24 h微博数据分析了地震灾区民众微博数量特征、情感极性特征、情绪时间序列特征、情绪反应空间分布特征。研究结果表明:芦山地震灾区民众负面情绪大于正面情绪,而九寨沟地震后民众正面情绪大于负面情绪,微博活跃数量程度与人口密度、生命线破坏程度、震中距离和烈度密切相关,微博活跃数量呈现空间分布不均衡特征。分析认为,2次地震后,灾区民众情感行为反应差异主要与灾区人口密度、房屋抗震性能、当地民众防震减灾意识、地震知识了解程度等密切相关。
Social perception technology is an effective means to study the temporal and spatial characteristics of people's behavior response in earthquake-strichen areas. Taking Lushan M7.0 earthquake in 2013 and Jiuzhaigou M7.0 earthquake in 2017 in Sichuan as examples,we obtain the 24 hours Weibo data after the earthquake by emotional dictionary and rules,and analyze the quantitative characteristics,the emotional polarity characteristics,temporal and spatial distribution characteristics of emotional response in the earthquake-stricken area. The results show that the negative emotions of the people in Lushan earthquake-stricken area are greater than the positive ones,and the positive ones in Jiuzhaigou earthquake are greater than the negative ones. The degree of microblog activity is closely related to population density,lifeline damage,epicenter distance and intensity and the spatial distribution of it is unbalanced. The main reasons for the difference of people's emotional and behavioral responses in the two earthquake-stricken areas are closely related to the population density,the seismic performance of buildings,the awareness of earthquake prevention and disaster reduction of local people,and the awareness of earthquake science popularization.
Social perception technology is an effective means to study the temporal and spatial characteristics of people's behavior response in earthquake-strichen areas. Taking Lushan M7.0 earthquake in 2013 and Jiuzhaigou M7.0 earthquake in 2017 in Sichuan as examples,we obtain the 24 hours Weibo data after the earthquake by emotional dictionary and rules,and analyze the quantitative characteristics,the emotional polarity characteristics,temporal and spatial distribution characteristics of emotional response in the earthquake-stricken area. The results show that the negative emotions of the people in Lushan earthquake-stricken area are greater than the positive ones,and the positive ones in Jiuzhaigou earthquake are greater than the negative ones. The degree of microblog activity is closely related to population density,lifeline damage,epicenter distance and intensity and the spatial distribution of it is unbalanced. The main reasons for the difference of people's emotional and behavioral responses in the two earthquake-stricken areas are closely related to the population density,the seismic performance of buildings,the awareness of earthquake prevention and disaster reduction of local people,and the awareness of earthquake science popularization.
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地震有感范围提取研究[J].地震研究,40(2):185-192.
曹彦波.2018.基于新浪微博的通海5.0级地震舆情时空特征分析[J].地震研究,41(4):525-533.
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.
李德仁.2016.展望大数据时代的地球空间信息学[J].测绘学报,45(4):379-384.
李清敏,张华平.2014.面向话题的中文微博观点倾向性分析研究[J].科学技术与工程,14(12):227-231.
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.
刘经南,方媛,郭迟,等.2014.位置大数据的分析处理研究进展[J].武汉大学学报:信息科学版,39(4):379-385.
刘瑜.2016.社会感知视角下的若干人文地理学基本问题再思考[J].地理学报,71(4):564-575.
钮成明,詹国华,李志华.2018.基于深度神经网络的微博文本情感倾向性分析[J].计算机系统应用,27(11):205-210.
庞磊,李寿山,周国栋.2012.基于情绪知识的中文微博情感分类方法[J].计算机工程,38(13):156-158.
苏晓慧,张群燕,张晓东,等.2013.基于微博的芦山地震前后宏观异常信息筛选与分析[J].震灾防御技术,8(4):451-458.
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.
王华.2018.基于主题模型和支持向量机的文本情感分类方法[J].宁德师范学院学报(自然科学版),30(3):246-254.
王磊.2018.基于最大熵的中文词语情感分析研究[J].计算机时代,(12):7-11.
王艳东,李昊,王腾,等.2016.基于社交媒体的突发事件应急信息挖掘与分析[J].武汉大学学报(信息科学版),46(3):290-297.
吴志峰,柴彦威,党安荣,等.2015.地理学碰上“大数据”:热反应与冷思考[J].地理研究,34(12):2207-2221.
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.
姚天昉,娄德成.2007.汉语语句主题语义倾向分析方法的研究[J].中文信息学报,21(5):73-79.
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.
Bengtsson L,Lu X,Thorson A,et al.2011.Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data:a Post-Earthquake Geospatial Study in Haiti[J].PLoS Medicine,8(8).doi:10.1371/journal.pmed.1001083.
Cheng J W,Mitomo H,Otsuka T,et al.2016.Cultivation Effects of Mass and Social Media on Perceptions and Behavioural Intentions in Post-Disaster Recovery-the Case of the 2011 Great East Japan Earthquake[J].Telematics and Informatics,33(3):753-772.
Comunello F,Parisi L,Lauciani V,et al.2016.Tweeting after an earthquake:User localization and communication patterns during the 2012 Emilia seismic sequence[J].Annals of geophysics = Annali di geofisica,59(5).doi:10.4401/ag-6945.
Crooks A,Croitoru A,Stefanidis A,et al.2013.Earthquake:Twitter as a Distributed Sensor System[J].Transactions in GIS,17(1):124-147.
Li L F,Zhang Q P,Tian J,et al.2018.Characterizing Information Propagation Patterns in Emergencies:a Case Study with Yiliang Earthquake[J].International Journal of Information Management,38(1):34-41.
Li X H,Wang Z,Gao C,Shi L.2017.Reasoning Human Emotional Responses from Large-Scale Social and Public Media[J].Applied Mathematics and Computation,310:182-193.
Liu Y,Liu X,Gao S,et al.2015.Social sensing:a New Approach to Understanding Our Socio-Economic Environments[J].Annals of the Association of American Geographers,105(3):512-530.
Lu X,Bengtsson L,Holme P.2012.Predictability of Population Displacement after the 2010 Haiti Earthquake[J].Proceedings of the National Academy of Sciences of the United States of America,109(29):11576-11581.
Onook O,Kyounghee H K,Rao H R.2010.An Exploration of Social Media in Extreme Events:Rumor Theory and Twitter During the Haiti Earthquake[C].Saint Louis,VSA:Proceedings of Thirty-first International Conference on Information Systems,231.
Qu Y,Huang C,Zhang P Y,et al.2011.Microblogging after a Major Disasterin China:a Case Study of the 2010 Yushu Earthquake[C].Hangzhou,China.Proceedings of the ACM Conference on Computer Supported Cooperative Work(CSCW11).25-34.
Sakaki T,Okazaki M,Matsuo Y.2013.Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development[J].IEEE Transactions on Knowledge And Data Engineering,25(4):919-931.
Thapa L.2015.Spatial-Temporal Analysis of Social Media Data Related to Nepal Earthquake.The International Archives of the Photogrammetry[J].Remote Sensing and Spatial Information Sciences,XLI(B2):567-571.
Yusuke H.2015.Behaviour Analysis Using Tweet Data and Geo-Tag Data in a Natural Disaster[J].Transportation Research Procedia,(11):399-412.
曹彦波,吴艳梅,许瑞杰,等.2017b.基于微博舆情数据的地震有感范围提取研究[J].地震研究,40(2):185-192.
曹彦波.2018.基于新浪微博的通海5.0级地震舆情时空特征分析[J].地震研究,41(4):525-533.
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.
李德仁.2016.展望大数据时代的地球空间信息学[J].测绘学报,45(4):379-384.
李清敏,张华平.2014.面向话题的中文微博观点倾向性分析研究[J].科学技术与工程,14(12):227-231.
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.
刘经南,方媛,郭迟,等.2014.位置大数据的分析处理研究进展[J].武汉大学学报:信息科学版,39(4):379-385.
刘瑜.2016.社会感知视角下的若干人文地理学基本问题再思考[J].地理学报,71(4):564-575.
钮成明,詹国华,李志华.2018.基于深度神经网络的微博文本情感倾向性分析[J].计算机系统应用,27(11):205-210.
庞磊,李寿山,周国栋.2012.基于情绪知识的中文微博情感分类方法[J].计算机工程,38(13):156-158.
苏晓慧,张群燕,张晓东,等.2013.基于微博的芦山地震前后宏观异常信息筛选与分析[J].震灾防御技术,8(4):451-458.
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.
王华.2018.基于主题模型和支持向量机的文本情感分类方法[J].宁德师范学院学报(自然科学版),30(3):246-254.
王磊.2018.基于最大熵的中文词语情感分析研究[J].计算机时代,(12):7-11.
王艳东,李昊,王腾,等.2016.基于社交媒体的突发事件应急信息挖掘与分析[J].武汉大学学报(信息科学版),46(3):290-297.
吴志峰,柴彦威,党安荣,等.2015.地理学碰上“大数据”:热反应与冷思考[J].地理研究,34(12):2207-2221.
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.
姚天昉,娄德成.2007.汉语语句主题语义倾向分析方法的研究[J].中文信息学报,21(5):73-79.
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.
Bengtsson L,Lu X,Thorson A,et al.2011.Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data:a Post-Earthquake Geospatial Study in Haiti[J].PLoS Medicine,8(8).doi:10.1371/journal.pmed.1001083.
Cheng J W,Mitomo H,Otsuka T,et al.2016.Cultivation Effects of Mass and Social Media on Perceptions and Behavioural Intentions in Post-Disaster Recovery-the Case of the 2011 Great East Japan Earthquake[J].Telematics and Informatics,33(3):753-772.
Comunello F,Parisi L,Lauciani V,et al.2016.Tweeting after an earthquake:User localization and communication patterns during the 2012 Emilia seismic sequence[J].Annals of geophysics = Annali di geofisica,59(5).doi:10.4401/ag-6945.
Crooks A,Croitoru A,Stefanidis A,et al.2013.Earthquake:Twitter as a Distributed Sensor System[J].Transactions in GIS,17(1):124-147.
Li L F,Zhang Q P,Tian J,et al.2018.Characterizing Information Propagation Patterns in Emergencies:a Case Study with Yiliang Earthquake[J].International Journal of Information Management,38(1):34-41.
Li X H,Wang Z,Gao C,Shi L.2017.Reasoning Human Emotional Responses from Large-Scale Social and Public Media[J].Applied Mathematics and Computation,310:182-193.
Liu Y,Liu X,Gao S,et al.2015.Social sensing:a New Approach to Understanding Our Socio-Economic Environments[J].Annals of the Association of American Geographers,105(3):512-530.
Lu X,Bengtsson L,Holme P.2012.Predictability of Population Displacement after the 2010 Haiti Earthquake[J].Proceedings of the National Academy of Sciences of the United States of America,109(29):11576-11581.
Onook O,Kyounghee H K,Rao H R.2010.An Exploration of Social Media in Extreme Events:Rumor Theory and Twitter During the Haiti Earthquake[C].Saint Louis,VSA:Proceedings of Thirty-first International Conference on Information Systems,231.
Qu Y,Huang C,Zhang P Y,et al.2011.Microblogging after a Major Disasterin China:a Case Study of the 2010 Yushu Earthquake[C].Hangzhou,China.Proceedings of the ACM Conference on Computer Supported Cooperative Work(CSCW11).25-34.
Sakaki T,Okazaki M,Matsuo Y.2013.Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development[J].IEEE Transactions on Knowledge And Data Engineering,25(4):919-931.
Thapa L.2015.Spatial-Temporal Analysis of Social Media Data Related to Nepal Earthquake.The International Archives of the Photogrammetry[J].Remote Sensing and Spatial Information Sciences,XLI(B2):567-571.
Yusuke H.2015.Behaviour Analysis Using Tweet Data and Geo-Tag Data in a Natural Disaster[J].Transportation Research Procedia,(11):399-412.