突发事件应急行动支撑信息的自动识别与分类研究

doi:10.3772/j.issn.1000-0135.2021.08.003

情报学报

2021, Vol. 40

Issue (8): 817-830 DOI: 10.3772/j.issn.1000-0135.2021.08.003

情报分析方法与技术

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突发事件应急行动支撑信息的自动识别与分类研究

吴雪华, 毛进, 陈思菁, 谢豪, 李纲

武汉大学信息资源研究中心，武汉 430072

Research on Automatic Identification and Classification of Actionable Information in Emergencies

Wu Xuehua, Mao Jin, Chen Sijing, Xie Hao, Li Gang

Center for Studies of Information Resources, Wuhan University, Wuhan 430072

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摘要突发事件场景中，应急行动支撑信息有助于各类主体的及时响应和敏捷动员，对于保障人员搜救、资源配置等各项应急行动的顺利开展，以及最小化伤亡损失具有重要意义。本文旨在探究如何从社交媒体中获取各类应急行动支撑信息。在梳理应急行动支撑信息概念、特征和类别的基础上，提出基于机器学习的应急行动支撑信息识别与分类的两阶段实现框架。基于文本向量表示、语言、形式和用户四个维度的特征，采用支持向量机、逻辑回归、文本卷积神经网络（text convolutional neural networks，TextCNN）、BERT（bidirectional encoder representations from transformers）以及BERT和TextCNN的组合模型（BERT+TextCNN）在人工标注数据集上开展实验，评估不同分类方法、算法和特征的效果。研究结果表明，两阶段方法在不损失模型性能的情况下，能够提供层次更为丰富的应急行动支撑信息。BERT和BERT+TextCNN在两个阶段任务中均优于其他模型。语言、形式和用户特征的组合对第一阶段任务——应急行动支撑信息识别无明显作用，但能显著提升第二阶段任务——应急行动支撑信息分类的效果。本研究有助于更好地将社交媒体数据流嵌入应急工作流程，在一定程度上缓解应急行动中的信息过载问题，提高应急效率。

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关键词 ：应急行动支撑信息, 突发事件, 文本分类, 机器学习, 社交媒体

收稿日期: 2020-08-13

基金资助:国家自然科学基金重大项目课题“国家安全大数据综合信息集成与分析方法”（71790612）；国家自然科学基金创新研究群体项目“信息资源管理”（71921002）。

作者简介: 吴雪华，女，1997年生，博士研究生，研究方向为应急情报、数据挖掘；毛进，男，1988年生，副教授，研究方向为信息组织、知识网络；陈思菁，女，1992年生，博士研究生，研究方向为危机信息学、社交网络，E-mail：csj16912@whu.edu.cn；谢豪，男，1998年生，硕士研究生，研究方向为多模态数据融合；李纲，男，1966年生，教授，博士生导师，研究方向为竞争情报、数据挖；

引用本文:

吴雪华, 毛进, 陈思菁, 谢豪, 李纲. 突发事件应急行动支撑信息的自动识别与分类研究[J]. 情报学报, 2021, 40(8): 817-830.
Wu Xuehua, Mao Jin, Chen Sijing, Xie Hao, Li Gang. Research on Automatic Identification and Classification of Actionable Information in Emergencies. 情报学报, 2021, 40(8): 817-830.

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1 蒋勋, 苏新宁, 刘喜文. 突发事件驱动的应急决策知识库结构研究[J]. 情报资料工作, 2015(1): 25-29.
2 Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes Twitter users: real-time event detection by social sensors[C]// Proceedings of the 19th International Conference on World Wide Web. New York: ACM Press, 2010: 851-860.
3 Imran M, Castillo C, Diaz F, et al. Processing social media messages in mass emergency: a survey[J]. ACM Computing Surveys, 2015, 47(4): 1-38.
4 Vieweg S, Hughes A L, Starbird K, et al. Microblogging during two natural hazards events: what twitter may contribute to situational awareness[C]// Proceedings of the 28th International Conference on Human Factors in Computing Systems. New York: ACM Press, 2010: 1079-1088.
5 Zade H, Shah K, Rangarajan V, et al. From situational awareness to actionability: towards improving the utility of social media data for crisis response[J]. Proceedings of the ACM on Human-Computer Interaction, 2018, 2(CSCW): Article No. 195.
6 Imran M, Castillo C, Diaz F, et al. Processing social media messages in mass emergency: survey summary[C]// Companion Proceedings of the Web Conference 2018. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee, 2018: 507-511.
7 Kropczynski J, Grace R, Coche J, et al. Identifying actionable information on social media for emergency dispatch[C]// Proceedings of the 2018 International Conference on Information Systems for Crisis Response and Management Asia Pacific. Auckland: Massey University, 2018: 428-438.
8 Dailey D, Starbird K. Addressing the information needs of crisis-affected communities: the interplay of legacy media and social media in a rural disaster[M]// The Communication Crisis in America, and How to Fix It. New York: Palgrave Macmillan, 2016: 285-303.
9 Purohit H, Castillo C, Diaz F, et al. Emergency-relief coordination on social media: automatically matching resource requests and offers[J/OL]. First Monday, 2013, 19(1). https://doi.org/10.5210/fm.v19i1.4848.
10 Sit M A, Koylu C, Demir I. Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma[J]. International Journal of Digital Earth, 2019, 12(11): 1205-1229.
11 仇培元, 陆锋, 张恒才, 等. 蕴含地理事件微博客消息的自动识别方法[J]. 地球信息科学学报, 2016, 18(7): 886-893.
12 Imran M, Elbassuoni S, Castillo C, et al. Extracting information nuggets from disaster-related messages in social media[C]// Proceedings of the 10th International Conference on Information Systems for Crisis Response and Management. ISCRAM Association, 2013.
13 李纲, 陈思菁, 毛进, 等. 自然灾害事件微博热点话题的时空对比分析[J]. 数据分析与知识发现, 2019, 3(11): 1-15.
14 Rudra K, Ganguly N, Goyal P, et al. Extracting and summarizing situational information from the Twitter social media during disasters[J]. ACM Transactions on the Web, 2018, 12(3): 1-35.
15 Munro R. Subword and spatiotemporal models for identifying actionable information in Haitian Kreyol[C]// Proceedings of the Fifteenth Conference on Computational Natural Language Learning. Stroudsburg: Association for Computational Linguistics, 2011: 68-77.
16 Baweja S, Aggarwal A, Goyal V, et al. Automatic retrieval of actionable information from disaster-related microblogs[J]. CEUR Workshop Proceedings, 2017, 2036: 43-45.
17 Kiatpanont R, Tanlamai U, Chongstitvatana P. Extraction of actionable information from crowdsourced disaster data[J]. Journal of Emergency Management, 2016, 14(6): 377-390.
18 Nguyen M T, Kitamoto A, Nguyen T T. TSum4act: a framework for retrieving and summarizing actionable tweets during a disaster for reaction[C]// Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Cham: Springer, 2015: 64-75.
19 Ghosh S, Srijith P K, Desarkar M S. Using social media for classifying actionable insights in disaster scenario[J]. International Journal of Advances in Engineering Sciences and Applied Mathematics, 2017, 9(4): 224-237.
20 Zahra K, Imran M, Ostermann F O. Automatic identification of eyewitness messages on twitter during disasters[J]. Information Processing & Management, 2020, 57(1): 102107.
21 夏华林, 张仰森. 基于规则与统计的Web突发事件新闻多层次分类[J]. 计算机应用, 2012, 32(2): 392-394, 415.
22 Ao J, Zhang P, Cao Y N. Estimating the locations of emergency events from Twitter streams[J]. Procedia Computer Science, 2014, 31: 731-739.
23 Habdank M, Rodehutskors N, Koch R. Relevancy assessment of tweets using supervised learning techniques: mining emergency related tweets for automated relevancy classification[C]// Proceedings of the 4th International Conference on Information and Communication Technologies for Disaster Management. New York: IEEE, 2017: 1-8.
24 Kaufhold M A, Bayer M, Reuter C. Rapid relevance classification of social media posts in disasters and emergencies: a system and evaluation featuring active, incremental and online learning[J]. Information Processing & Management, 2020, 57(1): 102132.
25 Pohl D, Bouchachia A, Hellwagner H. Batch-based active learning: application to social media data for crisis management[J]. Expert Systems with Applications, 2018, 93: 232-244.
26 Huang Q Y, Xiao Y. Geographic situational awareness: mining tweets for disaster preparedness, emergency response, impact, and recovery[J]. ISPRS International Journal of Geo-Information, 2015, 4(3): 1549-1568.
27 王艳东, 李昊, 王腾, 等. 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, 2016, 41(3): 290-297.
28 Kozlowski D, Lannelongue E, Saudemont F, et al. A three-level classification of French tweets in ecological crises[J]. Information Processing & Management, 2020, 57(5): 102284.
29 刘淑涵, 王艳东, 付小康. 利用卷积神经网络提取微博中的暴雨灾害信息[J]. 地球信息科学学报, 2019, 21(7): 1009-1017.
30 Yu M Z, Huang Q Y, Qin H, et al. Deep learning for real-time social media text classification for situation awareness-using Hurricanes Sandy, Harvey, and Irma as case studies[J]. International Journal of Digital Earth, 2019, 12(11): 1230-1247.
31 Zahera H M, Elgendy I, Jalota R, et al. Fine-tuned BERT model for multi-label tweets classification[C/OL]// Proceedings of the Twenty-Eighth Text REtrieval Conference, 2019. https://trec.nist.gov/pubs/trec28/papers/DICE_UPB.IS.pdf.
32 European Network and Security Agency. Actionable information for security incident response[EB/OL]. [2020-03-16]. https://www.enisa.europa.eu/topics/csirt-cert-services/reactive-services/copy_of_actionable-information.
33 Spasojevic N, Rao A. Identifying actionable messages on social media[C]// Proceedings of the 2015 IEEE International Conference on Big Data. New York: IEEE, 2015: 2273-2281.
34 Cao L B, Zhang C Q. The evolution of KDD: towards domain-driven data mining[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2007, 21(4): 677-692.
35 Garg H, Bansal C, Kaushal R, et al. Identifying actionable information from social media for better government-public relationship[C]// Proceedings of the 10th International Conference on Developments in eSystems Engineering. New York: IEEE, 2017: 206-211.
36 Tyshchuk Y, Wallace W A. Actionable information during extreme events—case study: warnings and 2011 Tohoku earthquake[C]// Proceedings of the 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing. New York: IEEE, 2012: 338-347.
37 Gerwin L E. The challenge of providing the public with actionable information during a pandemic[J]. The Journal of Law, Medicine & Ethics, 2012, 40(3): 630-654.
38 Pennington J, Socher R, Manning C D. GloVe: global vectors for word representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1532-1543.
39 Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2019, 1: 4171-4186.
40 Olteanu A, Vieweg S, Castillo C. What to expect when the unexpected happens: social media communications across crises[C]// Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. New York: ACM Press, 2015: 994-1009.
41 Imran M, Mitra P, Castillo C. Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages[C]// Proceedings of the Tenth International Conference on Language Resources and Evaluation. Paris: European Language Resources Association, 2016: 1638-1643.
42 Landis J R, Koch G G. The measurement of observer agreement for categorical data[J]. Biometrics, 1977, 33(1): 159-174.
43 Wu Y H, Schuster M, Chen Z F, et al. Google's neural machine translation system: bridging the gap between human and machine translation[OL]. (2016-10-08). https://arxiv.org/pdf/1609.08144v2. pdf.
44 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 40-41.
45 Nadeau C, Bengio Y. Inference for the generalization error[J]. Machine Learning, 2003, 52(3): 239-281.
46 Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1995, 57(1): 289-300.
47 Tausczik Y R, Pennebaker J W. The psychological meaning of words: LIWC and computerized text analysis methods[J]. Journal of Language and Social Psychology, 2010, 29(1): 24-54.
48 Dutt R, Basu M, Ghosh K, et al. Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities[J]. Information Processing & Management, 2019, 56(5): 1680-1697.