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SPATIOTEMPORAL DISTRIBUTION OF COVID-19 AND PUBLIC ANXIETY: ANALYSIS BASED ON MICRO-BLOG DATA |
CHANG Jian-xia1,2, LI Jun-yi1,2 |
1. School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China;
2. Shaanxi Key Laboratory of Tourism Informatics, Xi'an 710119, China |
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Abstract Based on the micro-blog texts of 17 cities in Henan Province, this paper analyzes the topic change of public anxiety by using semantic network analysis. On the basis of calculating the anxiety sentiment, this paper explores the temporal change of public anxiety with the development of COVID-19, and uses GIS to visualize the spatial distribution of public anxiety in 17 cities. The results show that:1) There is a positive correlation between the fluctuation range of anxiety and the epidemic. At the early stage, the public is more sensitive to the change of epidemic data. A small increase or decrease in the newly diagnosed cases can cause a sharp rise or fall in public anxiety. In the late stage, the impact gradually stabilizes. 2) In the early and late stage of the COVID-19, anxiety and the number of newly diagnosed cases changed simultaneously. In the middle two stages, the change of anxiety lagged behind the diagnosed cases number by 1-3days. 3) The focus topic of public concern is different by anxiety change. The occurrence and development of the epidemic will trigger public anxiety. Nevertheless, the anxiety will not subside with the decline of the epidemic. It will be amplified and transferred to the social anxiety in daily life, and may exist in the affected individuals for a long time. 4) The spatial distribution of public anxiety is affected by the epidemic data, and further by location, economic connection, traffic connection, population flow, epidemic response measures, etc.
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Received: 06 May 2020
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[1] |
Shigemura J, Ursano R J, Morganstein J C, et al. Public responses to the novel 2019 coronavirus (2019-nCoV) in Japan:Mental health consequences and target populations[J]. Psychiatry and Clinical Neurosciences, 2020,74(4):281-282.
|
[2] |
Wang C, Pan R, Wan X, et al. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China[J]. International Journal of Environmental Research and Public Health, 2020,17(5):1729-1733.
|
[3] |
彭方奇,郝永华.突发事件中的政府信息发布与舆情应对——以"8·12"天津港爆炸事件14次发布会为例[J].新闻研究导刊,2016, 7(9):14-15.[Peng Fangqi, Hao Yonghua. Government information release and public opinion response in emergencies:Taking 14 press conferences of Tianjin Port explosion on August 12 as an example[J]. Journal of News Research, 2016,7(9):14-15.]
|
[4] |
United Nations(UN). Shared Responsibility, Gobal Solidarity:Responding to the Socio-economic Impacts of COVID-19[R]. New York:United Nations(un), 2020:20-21.
|
[5] |
Han X, Wang J, Zhang M, et al. Using social media to mine and analyze public opinion related to COVID-19 in China[J]. International Journal of Environmental Research and Public Health, 2020,17(8):2788-2809.
|
[6] |
Yigitcanlar T, Kankanamge N, Preston A, et al. How can social media analytics assist authorities in pandemic-related policy decisions? Insights from Australian states and territories[J]. Health Information Science and Systems, 2020,8(1):37-57.
|
[7] |
王俊秀,杨宜音.社会心态蓝皮书[R].北京:社会科学文献出版社, 2015:224-228.[Wang Junxiu, Yang Yiyin. Blue Book of Social Mentality[R]. Beijing:Social Sciences Academic Press (China), 2015:224-228.]
|
[8] |
赖凯声,亓莉敏,陈浩,等.时势造英雄还是英雄造时势?精英与大众的微博情绪关系[C]//中国心理学会.第十七届全国心理学学术会议论文摘要集.北京:中国心理学会,2014:798-799.[Lai Kaisheng, Qi Limin, Chen Hao, et al. Times produce heroes or heroes shape the times? The emotion relationship between elites and masses on Weibo[C]//Chinese Psychological Society. Proceedings of the 17th National Psychological Academic Conference. Beijing:Chinese Psychological Society, 2014:798-799.]
|
[9] |
Panagiotopoulos P, Barnett J, Bigdeli A Z, et al. Social media in emergency management:Twitter as a tool for communicating risks to the public[J]. Technological Forecasting and Social Change, 2016, 111(10):86-96.
|
[10] |
涂海丽,唐晓波.基于在线评论的游客情感分析模型构建[J].现代情报,2016,36(4):70-77.[Tu Haili, Tang Xiaobo. Tourist sentiment analysis model building based on online reviews[J]. Journal of Modern Information, 2016,36(4):70-77.]
|
[11] |
刘思叶,田原,冯雨宁,等.游客微博主题情感分析方法比较研究[J]. 北京大学学报(自然科学版),2018,54(4):687-692.[Liu Siye, Tian Yuan, Feng Yuning, et al. Comparison of tourist thematic sentiment analysis methods based on Weibo data[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2018,54(4):687-692.]
|
[12] |
刘逸,保继刚,朱毅玲.基于大数据的旅游目的地情感评价方法探究[J].地理研究,2017,36(6):1091-1105.[Liu Yi, Bao Jigang, Zhu Yiling. Exploring emotion methods of tourism destination evaluation:A big-data approach[J]. Geographical Research, 2017,36(6):1091-1105.]
|
[13] |
邵培仁,林群.时间、空间、社会化——传播情感地理学研究的三个维度[J]. 中国传媒报告,2011,10(1):17-29.[Shao Peiren, Lin Qun. Three dimensions of time, space and socialization in the study of communication emotional geography[J]. China Media Report, 2011,10(1):17-29.]
|
[14] |
Golder S A, Macy M W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures[J]. Science, 2011,333(6051):1878-1881.
|
[15] |
于静.基于微博大数据的游客情感及时空变化研究——以西安为例[D]. 西安:陕西师范大学,2015:77-78.[Yu Jing. Research on Tourists' Emotion and Time-space Change Based on Micro-blog Big Data:Taking Xi'An as an Example[D]. Xi'an:Shaanxi Normal University, 2015:77-78.]
|
[16] |
马成,吴菲菲,陈舒博.武汉大学主校区情绪与压力时空分布特征研究[J].地理空间信息,2020,18(1):12-16.[Ma Cheng, Wu Feifei, Chen Shubo. Research on spatiotemporal distribution characteristics of emotion and pressure in the main campus of Wuhan University[J]. Geospatial Information, 2020,18(1):12-16.]
|
[17] |
Seltzer E K, Jean N S, Kramer-Golinkoff E, et al. The content of social media's shared images about Ebola:A retrospective study[J]. Public Health, 2015,129(9):1273-1277.
|
[18] |
Wang Z, Ye X, Tsou M H. Spatial, temporal, and content analysis of Twitter for wildfire hazards[J]. Natural Hazards, 2016,83(1):523-540.
|
[19] |
Ye X, Li S, Yang X, et al. Use of social media for the detection and analysis of infectious diseases in China[J]. ISPRS International Journal of Geo-Information, 2016,5(9):156-173.
|
[20] |
宗乾进,杨淑芳,谌莹,等.突发性灾难中受灾地区社交媒体用户行为研究——基于对"天津8.12爆炸"相关微博日志的内容分析和纵向分析[J].信息资源管理学报,2017,7(1):13-19.[Zong Qianjin, Yang Shufang, Chen Ying, et al. Behavior of social media users in disaster area under the outburst disasters:A content analysis and longitudinal study of explosion in Tianjin 12th August 2015[J]. Journal of Information Resources Management, 2017,7(1):13-19.]
|
[21] |
刘国巍,程国辉,姜金贵.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,2015, 34(6):126-130.[Liu Guowei, Cheng Guohui, Jiang Jingui. On the evolution of the unconventional emergency network public opinion from the perspective of spatial-temporal differentiation:Taking "Shanghai 12.31 Stampede" as an example[J]. Journal of Intelligence, 2015,34(6):126-130.]
|
[22] |
曹彦波,吴艳梅,许瑞杰,等.基于微博舆情数据的震后有感范围提取研究[J].地震研究,2017,40(2):303-310.[Cao Yanbo, Wu Yanmei, Xu Ruijie, et al. Research about the perceptible area extracted after the earthquake based on the micro-blog public opinion[J]. Journal of Seismological Research, 2017,40(2):303-310.]
|
[23] |
赵燕慧,路紫,张秋娈.多类型微博舆情时空分布关系的差异性及其地理规则[J].人文地理,2018,33(1):61-69.[Zhao Yanhui, Lu Zi, Zhang Qiuluan. The differences of spatial and temporal distribution relations in public opinion of multi-type micro-blog and its geographical rules[J]. Human Geography, 2018,33(1):61-69.]
|
[24] |
Lin Y, Margolin D, Wen X. Tracking and analyzing individual distress following terrorist attacks using social media streams[J]. Risk Analysis, 2017,37(8):1580-1605.
|
[25] |
Yang T, Xie J, Li G, et al. Social media big data mining and spatiotemporal analysis on public emotions for disaster mitigation[J]. ISPRS International Journal of Geo-Information, 2019,8(1):29-51.
|
[26] |
王昊,杨亮,林鸿飞.日本地震的微博热点事件分析[J].中文信息学报,2012,26(5):7-13.[Wang Hao, Yang Liang, Lin Hongfei. Hot event analysis of Japan earthquake on micro-blog[J]. Journal of Chinese Information Processing, 2012,26(5):7-13.]
|
[27] |
Li X, Wang Z, Gao C, et al. Reasoning human emotional responses from large-scale social and public media[J]. Applied Mathematics and Computation, 2017,310(10):182-193.
|
[28] |
Gruebner O, Lowe S R, Sykora M, et al. Spatio-temporal distribution of negative emotions in New York City after a natural disaster as seen in social media[J]. International Journal of Environmental Research and Public Health, 2018,15(10):2275-2287.
|
[29] |
Dhivya K, Bagavandas M. Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai, India, in 2015:A post hoc analysis[J]. International Journal of Health Geographics, 2020,19(1):19-32.
|
[30] |
Abdul A A, Alhuwail D, Househ M, et al. Top concerns of tweeters during the COVID-19 pandemic:Infoveillance study[J]. Journal of Medical Internet Research, 2020,22(4):16-22.
|
[31] |
Lwin M O, Lu J, Sheldenkar A, et al. Global sentiments surrounding the COVID-19 pandemic on Twitter:Analysis of Twitter trends[J]. JMIR Public Health and Surveillance, 2020,6(2):1-4.
|
[32] |
Jo W, Lee J, Park J, et al. Online information exchange and anxiety spread in the early stage of the novel coronavirus (COVID-19) outbreak in South Korea:Structural topic model and network analysis[J]. Journal of Medical Internet Research, 2020,22(6):455-474.
|
[33] |
Li Q, Wei C, Dang J, et al. Tracking and analyzing public emotion evolutions during COVID-19:A case study from the event-driven perspective on micro-blogs[J]. International Journal of Environmental Research and Public Health, 2020,17(18):68-92.
|
[34] |
Ahmed M Z, Ahmed O, Aibao Z, et al. Epidemic of COVID-19 in China and associated psychological problems[J]. Asian Journal of Psychiatry, 2020,51(5):92-98.
|
[35] |
Singh P, Sohal M, Dwivedi Y, et al. Psychological fear and anxiety caused by COVID-19:Insights from Twitter analytics[J]. Asian Journal of Psychiatry, 2020,54(8):280-281.
|
[36] |
杨振山,蔡建明.空间统计学进展及其在经济地理研究中的应用[J].地理科学进展,2010,29(6):757-768.[Yang Zhenshan, Cai Jianming. Progress of spatial statistics and its application in economic geography[J]. Progress in Geography, 2010,29(6):757-768.]
|
[37] |
Li S, Wang Y, Xue J, et al. The impact of COVID-19 epidemic declaration on psychological consequences:A study on active Weibo users[J]. International Journal of Environmental Research and Public Health, 2020,17(6):2032-2040.
|
[38] |
Zhu B, Zheng X, Liu H, et al. Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics[J]. Chaos, Solitons, and Fractals, 2020,140(11):110-123.
|
[39] |
Lydia K, Jim V O. Schizophrenia and urbanicity:A major environmental influence-Conditional on genetic risk[J]. Schizophrenia Bulletin, 2005,31(4):795-799.
|
[40] |
Spielberger C D, Gorsuch R L, Lushene R E. Manual for the Statetrait Anxiety Inventory (Self-evaluation Questionnare)[M]. Palo Alto:California Consulting Psychologists Press, 1970:3-20.
|
[41] |
刘鲁川,李旭,张冰倩.社交媒体用户的负面情绪与消极使用行为研究评述[J].情报杂志,2018,37(1):105-113.[Liu Luchuan, Li Xu, Zhang Bingqian. Research on negative emotions and passive use behaviors of social media users[J]. Journal of Intelligence, 2018, 37(1):105-113.]
|
[42] |
Onyeaka H K, Zahid S, Patel R S. The unaddressed behavioral health aspect during the coronavirus pandemic[J]. Cureus, 2020, 12(3):7351-7352.
|
[43] |
Su Y, Xue J, Liu X, et al. Examining the impact of COVID-19 lockdown in Wuhan and Lombardy:A psycholinguistic analysis on Weibo and Twitter[J]. International Journal of Environmental Research and Public Health, 2020,17(12):45-54.
|
[44] |
Wang C, Pan R, Wan X, et al. A longitudinal study on the mental health of general population during the COVID-19 epidemic in China[J]. Brain, Behavior, and Immunity, 2020,87(5):40-48.
|
[45] |
Hung M, Lauren E, Hon E S. Social network analysis of COVID-19 sentiments:Application of artificial intelligence[J]. Journal of Medical Internet Research, 2020,22(8):1-13.
|
[46] |
Li D, Chaudhary H, Zhang Z. Modeling spatiotemporal pattern of depressive symptoms caused by COVID-19 using social media data mining[J]. International Journal of Environmental Research and Public Health, 2020,17(14):4988-5009.
|
[47] |
Anderson K, Smith S J. Editorial:Emotional geographies[J]. Transactions of the Institute of British Geographers, 2010,26(1):7-10.
|
[48] |
Heimtun B. The holiday meal:Eating out alone and mobile emotional geographies[J]. Leisure Studies, 2010,29(2):175-192.
|
[49] |
Joshi G C, Paul M, Kalita B K, et al. Mapping the social landscape through social media[J]. Journal of Information Science, 2020, 46(6):776-789.
|
|
|
|