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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (6): 66-79    DOI: 10.11925/infotech.2096-3467.2020.1258
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Patterns and Evolution of Public Opinion on Weibo During Natural Disasters: Case Study of Typhoons and Rainstorms
Ma Yingxue,Zhao Jichang()
School of Economics and Management, Beihang University, Beijing 100191, China
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[Objective] This study reveals patterns and evolution of public opinion on Weibo during natural disasters from the perspectives of trending topics and information dissemination. [Methods] We proposed a machine learning approach to extract the valid data of natural disasters from Weibo. Then, we employed a deep learning model to cluster these textual posts. Finally, we investigated the information dissemination patterns with complex network analysis. [Results] The accuracy of our extractor for valid disaster information reached 0.82. The clusters of textual posts indicated the changes of trending topics. The structure of information dissemination during disasters was sparse. The sizes of online communities expanded constantly while their distribution unchanged. Users in different regions had different preferences for information sources. [Limitations] We did not conduct experiment to examine data from different social platforms. [Conclusions] The proposed method could effectively identify public opinion events during natural disasters.

Key wordsSocial Media      Topic Mining      Social Network      Public Opinion Management      Information Dissemination     
Received: 15 December 2020      Published: 06 July 2021
ZTFLH:  TP391  
Fund:National Natural Science Foundation of China(71871006)
Corresponding Authors: Zhao Jichang     E-mail:

Cite this article:

Ma Yingxue,Zhao Jichang. Patterns and Evolution of Public Opinion on Weibo During Natural Disasters: Case Study of Typhoons and Rainstorms. Data Analysis and Knowledge Discovery, 2021, 5(6): 66-79.

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Research Framework of Public Response Analysis During Disasters on Weibo
标签 查准率 查全率 F1值 样本量
0 0.81 0.64 0.71 201
1 0.83 0.92 0.88 399
Algorithm Performance on Typhoon Data Set
标签 查准率 查全率 F1值 样本量
0 0.75 0.67 0.71 188
1 0.86 0.90 0.88 412
Algorithm Performance on Rainstorm Data Set
Comparison of Data Volume Before and After Filtering
The Proportion of Forwarded Tweets
Algorithm Diagram of DEC Model
聚类标签 数量 占比/%
cluster_0 29 248 39.93
cluster_1 7 276 9.93
cluster_2 7 765 10.60
cluster_3 9 069 12.38
cluster_4 11 053 15.09
cluster_5 8 831 12.06
总量 73 242 100.00
Data Clustering Results Before Landing
聚类标签 数量 占比/%
cluster_0 6 542 12.87
cluster_1 7 415 14.59
cluster_2 7 050 13.87
cluster_3 17 342 34.12
cluster_4 5 665 11.15
cluster_5 6 808 13.40
总量 50 822 100.00
Data Clustering Results After Landing
聚类标签 前 20 关键词 文本描述
cluster_0 台风,吹到,装好,来场,丢人,那多,说走就走,行李,别人,旅行,信用卡,要来,万一,带上,期待,害怕,怎么办,听说,阳台,偷笑 轻松语境,偏生活、玩耍类
cluster_1 台风,登陆,台湾,尼伯特,强台风,新闻,原子弹,最强,网易,我国,沿海,台东,轰炸,福建,今年,阵风,华东,初台,分享,太麻里 台风登陆台湾、台风威力报道
cluster_2 台风,尼伯特,停运,影响,福建,列车,铁路,车票,旅客列车,部分,厦门,停售,福州,旅客,今年,沿海,强台风,航班,登陆,温福 台风造成的交通影响
cluster_3 台风,天空,尼伯特,天气,前夕,来临,吃惊,要来,微风,福州,这么,一个,今天,这个,厦门,可以,浮云,之前,真是,啤酒 多为无较大意义类短文本
cluster_4 台风,尼伯特,暴雨,预警,中心,华东,橙色,靠近,今年,安徽,湖北,发布,公里,台湾,今天,热带风暴,沿海,登陆,中央气象台,位于 台风播报和气象预警
cluster_5 台风,尼伯特,工作,响应,应急,防御,防汛,启动,防汛防台,做好,影响,今年,国家, 部署,强台风,防总,长江,可能,暴雨,准备 部署措施、应急防范类
Keywords and Descriptions of Different Clusters Before Landing
聚类标签 前 20 关键词 文本描述
cluster_0 台风,尼伯特,厦门,限贷,政策,第一号,楼市,国土资源,过后,过境,微笑,房产,看不出,来临,出去,管理局,感觉,明天,生变,厦门 生活类短文本
cluster_1 台风,尼伯特,影响,减弱,热带,低压,今天,今年,中心,暴雨,预计,福建,登陆,天气, 防汛,局部,宁化县,境内,气象台,福建省 台风播报及气象预报
cluster_2 台风,倒计时,登陆,景泰,拜拜,酸酸的,上海,草根,直通车,讲信用,哈哈,老头,警民,虎头蛇尾,江中,泥石流,卷入,市民,假期,谢谢 一些社会事件
cluster_3 台风,装好,吹到,说好,说走就走,丢人,万一,那多,行李,听说,旅行,有多严,要来,来场,带上,拜拜,信用卡,别人,害怕,期待 调侃类、口语化表达
cluster_4 台风,尼伯特,死亡,福建,失踪,万人,民政部,已致,受灾,福建省,台湾,紧急,新闻,造成,网易,台东,国家,转移, 因灾,今年 台风影响及灾害损失
cluster_5 台风,尼伯特,闽清,福建,福州,闽清县,影响,永泰,消防,洪水,救援,部分,灾区,今年,坂东镇,冲塌,坂东,严重,防抗,乡镇 受损及救灾情况
Keywords and Descriptions of Different Clusters After Landing
Posting Time of Different Clusters of Tweets
Emotions in Different Clusters of Tweets
聚类标签 转发占比 前 3 转发来源
cluster_0 0.10 @姚晨、@江宁公安在线、@北京厨子新号
cluster_1 0.11 @中国气象爱好者、@温州草根新闻、@中国天气
cluster_2 0.19 @南昌铁路、@新浪厦门、@南京发布
cluster_3 0.06 @青春影视偶像、@张鹤慈、@福州身边事儿
cluster_4 0.32 @央视新闻、@人民日报、@中央气象台
cluster_5 0.17 @头条新闻、@中国消防、@新浪上海
Forwarding Sources of Different Clusters Before Landing
聚类标签 转发占比 前 3 转发来源
cluster_0 0.06 @中央气象台、@中国气象爱好者、@中国铁路
cluster_1 0.12 @中国气象爱好者、@来去之间、@中央气象台
cluster_2 0.28 @警民直通车-上海、@人民日报、@福州日报
cluster_3 0.06 @美食上海站、@杭州微博城事、@天涯明月刀OL官微
cluster_4 0.16 @南昌铁路、@中国新闻网、@全球头条新闻事件
cluster_5 0.19 @新浪福建、@安徽消防、@福建身边事
Forwarding Sources of Different Clusters After Landing
台风名 总微博量 包含转发微博量 含转发的微博占比 总节点数 总边数 所有边权重和
尼伯特 165 871 26 212 15.80% 25 288 24 053 25 899
妮妲 108 956 9 497 8.72% 9 690 8 615 9 276
莫兰蒂 122 975 19 672 16.00% 17 112 16 232 19 273
鲇鱼 94 249 8 132 8.63% 7 927 7 090 7 935
海马 1141 46 9 570 8.38% 8 682 7 550 9 341
Features of Forwarding Networks
Sizes of Forwarding Networks Over Time
Size Distribution of Connected Components of Forwarding Networks Over Time
A Sample Randomly Extracted from a Forwarding Network
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