情报研究

自然灾害危机情境中政务微博的公众言语行为模式及情绪传播研究

  • 张敏 ,
  • 张芳 ,
  • 张可 ,
  • 孟欣欣
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  • 华中师范大学信息管理学院 武汉 430079
张敏,教授,博士,博士生导师,E-mail:zhangmin74@ccnu.edu.cn;张芳,硕士研究生;张可,博士研究生;孟欣欣,硕士研究生。

收稿日期: 2024-03-13

  修回日期: 2024-06-15

  网络出版日期: 2024-10-29

基金资助

本文系国家社会科学基金一般项目“政务社交媒体用户信息获取中的情感体验及效用研究”(项目编号:20BTQ048)研究成果之一。

Research on Public Speech Acts Patterns and Sentiment Dissemination in Government Microblogs in Natural Disaster Crisis

  • Zhang Min ,
  • Zhang Fang ,
  • Zhang Ke ,
  • Meng Xinxin
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  • School of Information Management, Central China Normal University, Wuhan 430079

Received date: 2024-03-13

  Revised date: 2024-06-15

  Online published: 2024-10-29

Supported by

This work is supported by the National Social Science Fund of China project titled “Research on Emotional Experience and Utility in Information Acquisition of Governmental Social Media Users” (Grant No. 20BTQ048).

摘要

[目的/意义] 自然灾害危机事件情境下公众言语行为和情绪传播分析有助于理解政务社交媒体用户的行为意图、行为模式和心理状态,为政府的危机应对和舆情管控提供理论支持和参考依据。[方法/过程] 综合运用定性内容编码、滞后序列分析、聚类分析、主题挖掘等研究方法,以2023年“台风杜苏芮”事件为例,对收集到的5个政务微博账号254个博文及5 036条评论数据进行细致分析。[结果/结论] 在自然灾害危机事件情境下公众言语行为类型主要集中在表达类和陈述类,同时存在信息、关系、情感3种主要言语行为模式;公众情感主要表现为同质性感染和负向延伸传播,且具有时间阶段性特征;不同言语行为模式的公众情绪水平具有差异性,信息模式下的情绪水平更低;“行动救援类”博文在 3种言语行为模式下都显著正向影响公众情绪水平。由于政务社交媒体公众参与具有动态性且参与过程很难被细致刻画,采用言语行为分类、滞后序列分析和自动聚类的方法能够为公众参与危机治理研究提供新的视角和方法。

本文引用格式

张敏 , 张芳 , 张可 , 孟欣欣 . 自然灾害危机情境中政务微博的公众言语行为模式及情绪传播研究[J]. 图书情报工作, 2024 , 68(20) : 104 -117 . DOI: 10.13266/j.issn.0252-3116.2024.20.009

Abstract

[Purpose/Significance] The analysis of public speech acts and sentiment dissemination in natural disaster crisis helps understand the acts intentions, acts patterns and psychological states of government social media users, and provides theoretical support and reference basis for governmental crisis management and public opinion control in crisis events. [Method/Process] Taking the “Typhoon Dusuri” event in 2023 as an example, this research integrated qualitative content coding, lag sequential analysis, cluster analysis, theme mining and other research methods to meticulously analyze 254 blog posts and 5036 comments from five government microblog accounts. [Result/Conclusion] In the natural disaster crisis, public speech acts are mainly concentrated in expressive and declarative categories, and there are three main speech act modes, information, relationship and sentiment at the same time. The public sentiment mainly shows homogeneous infection and negative extended transmission, characterized by temporal stages. Public sentiment levels vary across speech act modes, with information mode in lower sentiment level. The “action and rescue” blog topics significantly increase the public sentiment in all the three modes. Due to the dynamic nature of public participation in governmental social media and the difficulty of portraying the participation process in detail, the speech acts classification, lag sequence analysis and automatic clustering methods used in this study provide new perspectives and methods for public participation in crisis governance research.

参考文献

[1] 周利敏, 钟娇文. 应急管理中社交媒体的嵌入:理论构建与实践创新[J]. 中国行政管理, 2022(1): 121-127. (ZHOU L M, ZHONG J W. The embedding of social media in emergency management: theoretical construction and practical innovation[J]. Chinese public administration, 2022(1): 121-127.)
[2] 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: 86-96.
[3] BUKAR U A, JABAR M A, SIDI F, et al. Crisis informatics in the context of social media crisis communication: theoretical models, taxonomy, and open issues[J]. IEEE access, 2020, 8: 185842-185869.
[4] SEARLE J. Mind, language and society [M]. New York: Basic Books, 1998.
[5] VOSOUGHI S, ROY D. Tweet acts: a speech act classifier for Twitter[C]//Proceedings of the international AAAI conference on Web and social media. Cologne: Association for the Advancement of Artificial Intelligence, 2016, 10(1): 711-714.
[6] SAHA T, JAYASHREE S R, SAHA S, et al. BERT-caps: a transformer-based capsule network for Tweet act classification[J]. IEEE transactions on computational social systems, 2020, 7(5): 1168-1179.
[7] 孙冉, 安璐. 突发事件情境下社交媒体用户的言语行为分类研究[J]. 信息资源管理学报, 2023, 13(6): 99-109, 124. (SUN R, AN L. The speech acts classification of social media users in the context of public emergencies[J]. Journal of information resources management, 2023, 13(6): 99-109, 124.)
[8] SINGH T, OLIVARES S, COHEN T, et al. Pragmatics to reveal Intent in social media peer interactions: mixed methods study[J]. Journal of medical internet research, 2021, 23(11): e32167.
[9] 崔蓬克. 言语行为视角下的政府微博语言研究[D]. 上海: 华东师范大学, 2014. (CUI P K. Study in government Weibo utterances from the speech act theoretic perspective[D]. Shanghai: East China Normal University, 2014.)
[10] MARSEN S, ALI-CHAND Z. ‘We all have a role to play’: a comparative analysis of political speech acts on the COVID19 crisis in the south pacific: speech acts in crisis political discourse in the south pacific[J]. Communication research and practice, 2022, 8(1): 19-35.
[11] LI S, LIAO W, KIM C, et al. Understanding the association between online social support obtainment and coping during a public crisis[J]. Journal of health communication, 2022, 27(5): 343-352.
[12] YAO Z, TANG P, FAN J, et al. Influence of online social support on the public's belief in overcoming COVID-19[J]. Information processing & management, 2021, 58(4): 102583.
[13] WUKICH C. Social media engagement forms in government: a structure-content framework[J]. Government information quarterly, 2022, 39(2): 101684.
[14] CHEN Q, MIN C, ZHANG W, et al. Unpacking the black box: how to promote citizen engagement through government social media during the COVID-19 crisis[J]. Computers in human behavior, 2020, 110: 106380.
[15] MEDAGLIA R, ZHU D. Public deliberation on government-managed social media: a study on Weibo users in China[J]. Government information quarterly, 2017, 34(3): 533-544.
[16] VILLODRE J, CRIADO J I. User roles for emergency management in social media: understanding actors' behavior during the 2018 Majorca island flash floods[J]. Government information quarterly, 2020, 37(4): 101521.
[17] DISTEL B, LINDGREN I. A matter of perspective: conceptualizing the role of citizens in E-government based on value positions[J]. Government information quarterly, 2023, 40(4): 101837.
[18] 安璐, 吴林. 融合主题与情感特征的突发事件微博舆情演化分析[J]. 图书情报工作, 2017, 61(15): 120-129. (AN L, WU L. An integrated analysis of topical and emotional evolution of microblog public opinions on public emergencies[J]. Library and information service, 2017, 61(15): 120-129.)
[19] 张琛, 马祥元, 周扬, 等. 基于用户情感变化的新冠疫情舆情演变分析[J]. 地球信息科学学报, 2021, 23(2): 341-350. (ZHANG C, MA X Y, ZHOU Y, et al. Analysis of public opinion evolution in COVID-19 pandemic from a perspective of sentiment variation[J]. Journal of geo-information science, 2021, 23(2): 341-350.)
[20] 鲁艳霞, 裘江南, 许莉薇. 突发自然灾害中信息源对微博用户认知和情绪反应的影响研究——用户涉入的调节作用[J]. 管理评论, 2023, 35(10): 188-204. (LIU Y X, QIU J N, XU L W. The influence of information source on Weibo users' cognitive and emotional responses in sudden natural disasters: the moderating effect of user involvement[J]. Management review, 2023, 35(10): 188-204.)
[21] 刘冰, 张航. 基于民众需求与情感的突发公共卫生事件政府回应策略研究[J]. 情报科学, 2023, 41(9): 8-18. (LIU B, ZHANG H.A study of government response strategies in public health emergencies based on people's needs and emotions[J]. Information science, 2023, 41(9): 8-18.)
[22] SAVOLAINEN R. Dialogue processes in online information seeking and sharing: a study of an asynchronous discussion group[J]. Information research, 2020, 25(3): 1-19.
[23] 张敏, 姜冠兰, 丁恒. 中文网络学术社区用户会话交互模式及其交互质量研究[J]. 情报学报, 2022, 41(12): 1314-1328. (ZHANG M, JIANG G L, DING H. Examining users' conversation interaction structure and quality in the Chinese online academic community[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(12): 1314-1328.)
[24] AUSTIN J L. How to do things with words[M]. Oxford: Clarendon Press, 1962.
[25] ZHANG X A, BORDEN J, KIM S. Understanding publics’ post-crisis social media engagement behaviors: an examination of antecedents and mediators[J]. Telematics and informatics, 2018, 35(8): 2133-2146.
[26] TSOUMOU J M. Analyzing speech acts in politically related Facebook communication[J]. Journal of pragmatics, 2020, 167: 80-97.
[27] 徐琳宏, 林鸿飞, 潘宇, 等. 情感词汇本体的构造[J]. 情报学报, 2008, 27(2): 180-185. (XU L H, LIN H F, PAN Y, et al. The construction of an emotional vocabulary ontology[J]. Journal of the China Society for Scientific and Technical Information, 2008, 27(2): 180-185.)
[28] BAKEMAN R, GOTTMAN J M. Observing interaction: an introduction to sequential analysis[M]. Cambridge: Cambridge University Press, 1997.
[29] SUN Z, LIN C H, LV K, et al. Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences[J]. Educational technology research and development, 2021, 69: 533-551.
[30] 黄洛颖, 陈丽, 骆舒寒. cMOOC学习者教学交互转化的特征及演化研究[J]. 中国远程教育, 2022(5): 18-25, 55, 76. (HUANG L Y, CHEN L, LUO S H. Transformation in cMOOC learner interactions: features and evolution[J]. Chinese journal of distance education, 2022(5): 18-25, 55, 76.)
[31] POHL M, WALLNER G, KRIGLSTEIN S. Using lag-sequential analysis for understanding interaction sequences in visualizations[J]. International journal of human-computer studies, 2016, 96: 54-66.
[32] IKOTUN A M, EZUGWU A E, ABUALIGAH L, et al. K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data[J]. Information sciences, 2023(622): 178-210.
[33] FINK S. Crisis management: planning for the inevitable[M]. New York: American Management Association, 1986.
[34] 马腾, 殷跃, 赵树宽, 等. 多维数据融合的突发公共卫生事件网络舆情演化特征研究[J]. 情报理论与实践, 2022, 45(12): 170-177. (MA T, YIN Y, ZHAO S K, et al. Research on the evolution of network public opinion on public health emergencies based on multidimensional data fusion[J]. Information studies: theory & application, 2022, 45(12): 170-177.)
[35] GROOTENDORST M. BERTopic: neural topic modeling with a class-based TF-IDF procedure[J]. Arxiv preprint arxiv:2203.05794, 2022.
[36] CARR C T, SCHROCK D B, DAUTERMAN P. Speech acts within Facebook status messages[J]. Journal of language and social psychology, 2012, 31(2): 176-196.
[37] EPURE E V, COMPAGNO D, SALINESI C, et al. Process models of interrelated speech intentions from online health-related conversations[J]. Artificial intelligence in medicine, 2018, 91: 23-38.
[38] 卢恒, 张向先, 张莉曼, 等. 会话分析视角下虚拟学术社区用户交互行为特征研究[J]. 图书情报工作, 2020, 64(13): 80-89. (LU H, ZHANG X X, ZHANG L M, et al. Study on user interaction characteristics in virtual academic community from the perspective of conversation analysis[J]. Library and information service, 2020, 64(13): 80-89.)
[39] 谭金波, 吴思思, 吴磊, 等. 基于行为序列的“专家—新手”结对编程话语互动模式分析[J]. 电化教育研究, 2022, 43(7): 106-113. (TAN J B, WU S S, WU L, et al. Analysis of discourse interaction mode of "expert-novice" pair programming based on behavioral sequence[J]. e-Education research, 2022, 43(7): 106-113.)
[40] LI S, LIAO W, KIM C, et al. Understanding the association between online social support obtainment and coping during a public crisis[J]. Journal of health communication, 2022, 27(5): 343-352.
[41] YUAN Q, GASCO M. Citizens' use of microblogging during emergency: a case study on water contamination in Shanghai[C]//Proceedings of the 18th annual international conference on digital government research. New York: Association for Computing Machinery, 2017: 110-119.
[42] 曹彦波. 基于社交媒体的地震灾区民众情绪反应分析[J]. 地震研究, 2019, 42(2): 245-256. (CAO Y B. Analysis of people's emotional response in earthquake-stricken areas based on the social media[J]. Journal of seismological research, 2019, 42(2): 245-256.)
[43] ZHOU S, YANG X, WANG Y, et al. Affective agenda dynamics on social media: interactions of emotional content posted by the public, government, and media during the COVID-19 pandemic[J]. Humanities and social sciences communications, 2023, 10(1): 1-10.
[44] 裘江南, 葛一迪. 社交媒体情绪对信息行为的影响: 基于两类灾害事件的比较研究[J]. 管理科学, 2020, 33(1): 3-15. (QIU J N, GE Y D. Influence of emotions in social media on information behavior in two types of typical disasters[J]. Journal of management science, 2020, 33(1): 3-15.)
[45] 仲兆满, 李恒, 杨洪, 等. 基于传染病模型的突发事件网民情感演变分析[J]. 数据采集与处理, 2023, 38(3): 676-689. (ZHONG Z M, LI H, YANG H, et al. Analysis of the evolution of netizens' emotions in emergencies based on infectious disease model[J]. Journal of data acquisition and processing, 2023, 38(3): 676-689.)
[46] ZHANG M, DING S, LIU G, et al. Negativity bias in emergent online events: occurrence and manifestation[J]. Acta psychologica sinica, 2021, 53(12): 1361-1375.
[47] 韩小伟, 张传洋, 张起超, 等. 大数据背景下突发公共事件网络舆情情感演化及舆情引导策略研究[J]. 情报科学, 2024, 42(2): 56-63. (HAN X W, ZHANG C Y, ZHANG Q C, et al. The evolution of network public opinion emotions and public opinion guidance strategies for sudden public events under the background of big data[J]. Information science, 2024, 42(2): 56-63.
[48] 宋慎铭, 王琛, 詹东远. 突发公共卫生事件下的在线社交媒体公众情绪挖掘[J]. 管理评论, 2024, 36(3): 246-257. (SONG S M, WANG C, ZHAN D Y. Online social media public emotions mining during a public health emergency[J]. Management review, 2024, 36(3): 246-257.
[49] GE S, ZHANG J, HU C. Time to form a balanced risk prevention and control community between the government and individuals[J]. Science, 2020, 369(6503): 483.
[50] WANG N T, CARTE T A, BISEL R S. Negativity decontaminating: communication media affordances for emotion regulation strategies[J]. Information and organization, 2020, 30(2): 100299.
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