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Online-Media Situation Awareness: Development of Theoretical Framework Based on Systematic Literature Review |
Huang Meiyin1,2, Wang Fang1,2, Liu Qingmin1,2 |
1.Department of Information Resources Management, Business School, Nankai University, Tianjin 300071 2.Center for Network Society Governance, Nankai University, Tianjin 300071 |
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Abstract The situational awareness of online media has received attention from both industry and academia. However, the related studies are unorganized. By systematically reviewing the relevant literature, this study provides a systematic understanding of online-media situation awareness to promote communication between different fields. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) method is employed to obtain and organize SSCI (Social Sciences Citation Index)/SCI (Science Citation Index)/CSSCI (Chinese Social Sciences Citation Index) literature. After performing content coding and analysis under the three-level framework of “perception, comprehension, and projection,” the content of online-media situation awareness is refined. The perception layer includes content, time, space, and emotion perceptions. The comprehension layer includes an understanding of the target and hot events. The projection layer includes content burst, spatiotemporal anomaly, and emotional anomaly predictions. Subsequently, a four-level theoretical framework of “data, perception, comprehension, and projection” for online-media situation awareness is constructed, and its theoretical and practical significance is revealed.
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Received: 12 June 2023
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1 Endsley M R. Situation awareness global assessment technique (SAGAT)[C]// Proceedings of the IEEE 1988 National Aerospace and Electronics Conference. Piscataway: IEEE, 1988: 789-795. 2 Paul N R, Sahoo D, Balabantaray R C. Classification of crisis-related data on Twitter using a deep learning-based framework[J]. Multimedia Tools and Applications, 2023, 82(6): 8921-8941. 3 ben Khalifa M, Díaz Redondo R P, Vilas A F, et al. Identifying urban crowds using geo-located Social media data: a Twitter experiment in New York City[J]. Journal of Intelligent Information Systems, 2017, 48(2): 287-308. 4 Shan S Q, Zhao F, Wei Y G, et al. Disaster management 2.0: a real-time disaster damage assessment model based on mobile social media data—a case study of Weibo (Chinese Twitter)[J]. Safety Science, 2019, 115: 393-413. 5 Paradkar A S, Zhang C, Yuan F X, et al. Examining the consistency between geo-coordinates and content-mentioned locations in tweets for disaster situational awareness: a Hurricane Harvey study[J]. International Journal of Disaster Risk Reduction, 2022, 73: 102878. 6 Rossi C, Acerbo F S, Ylinen K, et al. Early detection and information extraction for weather-induced floods using social media streams[J]. International Journal of Disaster Risk Reduction, 2018, 30: 145-157. 7 Lu H, Zhu Y F, Shi K Z, et al. Using adverse weather data in social media to assist with city-level traffic situation awareness and alerting[J]. Applied Sciences, 2018, 8(7): 1193. 8 Wang Y, Taylor J E. DUET: data-driven approach based on latent Dirichlet allocation topic modeling[J]. Journal of Computing in Civil Engineering, 2019, 33(3): 04019023. 9 Alam F, Ofli F, Imran M. Processing social media images by combining human and machine computing during crises[J]. International Journal of Human-Computer Interaction, 2018, 34(4): 311-327. 10 Bosch H, Thom D, Heimerl F, et al. ScatterBlogs2: real-time monitoring of microblog messages through user-guided filtering[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 2022-2031. 11 Lu J, Zhang G Q, Wu F J. Team situation awareness using web-based fuzzy group decision support systems[J]. International Journal of Computational Intelligence Systems, 2008, 1(1): 50-59. 12 Evchina Y, Puttonen J, Dvoryanchikova A, et al. Context-aware knowledge-based middleware for selective information delivery in data-intensive monitoring systems[J]. Engineering Applications of Artificial Intelligence, 2015, 43: 111-126. 13 Liberati A, Altman D G, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration[J]. Journal of Clinical Epidemiology, 2009, 62(10): e1-e34. 14 Tai K T. Open government research over a decade: a systematic review[J]. Government Information Quarterly, 2021, 38(2): 101566. 15 马鑫, 王芳. 元宇宙的概念、技术、应用与影响——一项系统性文献综述[J]. 图书情报工作, 2023, 67(18): 113-128. 16 Peary B D M, Shaw R, Takeuchi Y. Utilization of social media in the East Japan Earthquake and Tsunami and its effectiveness[J]. Journal of Natural Disaster Science, 2012, 34(1): 3-18. 17 ?erban O, Thapen N, Maginnis B, et al. Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification[J]. Information Processing & Management, 2019, 56(3): 1166-1184. 18 曾大军, 曹志冬. 突发事件态势感知与决策支持的大数据解决方案[J]. 中国应急管理, 2013(11): 15-23. 19 张海涛, 栾宇, 周红磊, 等. 总体国家安全观下重大突发事件的智能决策情报体系研究[J]. 情报学报, 2022, 41(11): 1174-1187. 20 赵振营. 意识形态视角下网络舆情态势感知方法研究[J]. 情报科学, 2023, 41(1): 152-157, 173. 21 王秉, 史志勇, 周佳胜. 安全情报视域下的安全态势感知与塑造模型[J]. 情报理论与实践, 2023, 46(1): 1-6. 22 周欢, 张培颖, 黄晓怡, 等. 事件系统视角下网络舆情态势感知研究[J]. 情报杂志, 2024, 43(2): 135-142, 117. 23 张海涛, 周红磊, 李佳玮, 等. 信息不完全状态下重大突发事件态势感知研究[J]. 情报学报, 2021, 40(9): 903-913. 24 徐元, 毛进, 李纲. 面向突发事件应急管理的社交媒体多模态信息分析研究[J]. 情报学报, 2021, 40(11): 1150-1163. 25 李纲, 王施运, 毛进, 等. 面向态势感知的国家安全事件图谱构建研究[J]. 情报学报, 2021, 40(11): 1164-1175. 26 王施运, 李白杨, 白云, 等. 面向国家安全场景的态势感知与分析方法研究[J]. 情报理论与实践, 2021, 44(7): 178-183. 27 王伟, 杨建林, 梁继文. 融合情报思维的科技发展态势感知模式研究[J]. 情报学报, 2023, 42(3): 268-278. 28 Endsley M R. Measurement of situation awareness in dynamic systems[J]. Human Factors, 1995, 37(1): 65-84. 29 Ning H S, Liu H, Ma J H, et al. Cybermatics: Cyber-physical-social-thinking hyperspace based science and technology[J]. Future Generation Computer Systems, 2016, 56: 504-522. 30 You Y, Pekkola S. Meeting others—supporting situation awareness on the WWW[J]. Decision Support Systems, 2001, 32(1): 71-82. 31 Jiang L Y, Jayatilaka A, Nasim M, et al. Systematic literature review on cyber situational awareness visualizations[J]. IEEE Access, 2022, 10: 57525-57554. 32 Yin J, Lampert A, Cameron M, et al. Using social media to enhance emergency situation awareness[J]. IEEE Intelligent Systems, 2012, 27(6): 52-59. 33 Crooks A, Croitoru A, Stefanidis A, et al. #Earthquake: Twitter as a distributed sensor system[J]. Transactions in GIS, 2013, 17(1): 124-147. 34 Franke U, Brynielsson J. Cyber situational awareness-a systematic review of the literature[J]. Computers & Security, 2014, 46: 18-31. 35 张涛, 马海群. 智能情报分析中数据与算法风险识别模型构建研究[J]. 情报学报, 2022, 41(8): 832-844. 36 白如江, 张玉洁, 赵梦梦, 等. 面向关联推理的智慧情报感知: 内涵、组织与路径[J]. 情报理论与实践, 2022, 45(8): 31-37, 67. 37 Hevner A R, March S T, Park J, et al. Design science in information systems research[J]. MIS Quarterly, 2004, 28(1): 75-105. 38 吴雪华, 毛进, 陈思菁, 等. 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. 39 Koshy R, Elango S. Multimodal tweet classification in disaster response systems using transformer-based bidirectional attention model[J]. Neural Computing and Applications, 2023, 35(2): 1607-1627. 40 Li Y, Cheng J X, Huang C, et al. NEDetector: automatically extracting cybersecurity neologisms from hacker forums[J]. Journal of Information Security and Applications, 2021, 58: 102784. 41 Deng Q, Liu Y, Liu X D, et al. Social media usage during disasters: exploring the impact of location and distance on online engagement[J]. Disaster Medicine and Public Health Preparedness, 2020, 14(2): 183-191. 42 Lu H, Yuan S P. What motivates information sharing about disaster victims on social media? Exploring the role of compassion, sadness, expectancy violation, and enjoyment[J]. International Journal of Disaster Risk Reduction, 2021, 63: 102431. 43 刘青川, 关斌, 包国宪. 突发性公共卫生事件中居民的风险信息感知研究——基于全国81个城市的调研数据[J]. 图书与情报, 2021(2): 40-53. 44 Bubendorff S, Rizza C, Prieur C. Construction and dissemination of information veracity on French social media during crises: comparison of Twitter and Wikipedia[J]. Journal of Contingencies and Crisis Management, 2021, 29(2): 204-216. 45 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. 46 Lee C, Tien I. Probabilistic framework for integrating multiple data sources to estimate disaster and failure events and increase situational awareness[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2018, 4(4): 04018042. 47 Gross G A, Nagi R. Precedence tree guided search for the efficient identification of multiple situations of interest-AND/OR graph matching[J]. Information Fusion, 2016, 27: 240-254. 48 Zou Z Q, Gan H Y, Huang Q Y, et al. Disaster image classification by fusing multimodal social media data[J]. ISPRS International Journal of Geo-Information, 2021, 10(10): 636. 49 李纲, 张霁, 毛进, 等. 灾害事件下社交媒体图文相关性研究[J]. 情报学报, 2020, 39(11): 1223-1231. 50 Scheele C, Yu M Z, Huang Q Y. Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features[J]. International Journal of Digital Earth, 2021, 14(11): 1721-1743. 51 Xu S S, Li S N, Huang W. A spatial-temporal-semantic approach for detecting local events using geo-social media data[J]. Transactions in GIS, 2020, 24(1): 142-173. 52 Deng Q, Liu Y, Zhang H, et al. A new crowdsourcing model to assess disaster using microblog data in typhoon Haiyan[J]. Natural Hazards, 2016, 84(2): 1241-1256. 53 Alam F, Ofli F, Imran M. Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria[J]. Behaviour & Information Technology, 2020, 39(3): 288-318. 54 Fan C, Wu F S, Mostafavi A. A hybrid machine learning pipeline for automated mapping of events and locations from social media in disasters[J]. IEEE Access, 2020, 8: 10478-10490. 55 Yuan F X, Li M, Liu R, et al. Social media for enhanced understanding of disaster resilience during Hurricane Florence[J]. International Journal of Information Management, 2021, 57: 102289. 56 Avvenuti M, Cresci S, Del Vigna F, et al. CrisMap: a big data crisis mapping system based on damage detection and geoparsing[J]. Information Systems Frontiers, 2018, 20(5): 993-1011. 57 Shibuya Y, Tanaka H. Using social media to detect socio-economic disaster recovery[J]. IEEE Intelligent Systems, 2019, 34(3): 29-37. 58 Gao Y Z, Wang S W, Padmanabhan A, et al. Mapping spatiotemporal patterns of events using social media: a case study of influenza trends[J]. International Journal of Geographical Information Science, 2018, 32(3): 425-449. 59 Andrienko G, Andrienko N, Bosch H, et al. Thematic patterns in georeferenced tweets through space-time visual analytics[J]. Computing in Science & Engineering, 2013, 15(3): 72-82. 60 Wang Z Y, Ye X Y. Social media analytics for natural disaster management[J]. International Journal of Geographical Information Science, 2018, 32(1): 49-72. 61 Kryvasheyeu Y, Chen H H, Obradovich N, et al. Rapid assessment of disaster damage using social media activity[J]. Science Advances, 2016, 2(3): e1500779. 62 Saroj A, Pal S. Use of social media in crisis management: a survey[J]. International Journal of Disaster Risk Reduction, 2020, 48: 101584. 63 Singh N, Roy N, Gangopadhyay A. Analyzing the emotions of crowd for improving the emergency response services[J]. Pervasive and Mobile Computing, 2019, 58: 101018. 64 Wang J H, Fan Y C, Palacios J, et al. Global evidence of expressed sentiment alterations during the COVID-19 pandemic[J]. Nature Human Behaviour, 2022, 6(3): 349-358. 65 Spinsanti L, Ostermann F. Automated geographic context analysis for volunteered information[J]. Applied Geography, 2013, 43: 36-44. 66 Kim J, Hastak M. Online human behaviors on social media during disaster responses[J]. Homeland Security Affairs, 2017, 13(SI): Article 14135. 67 Fan C, Jiang Y C, Mostafavi A. Social sensing in disaster city digital twin: integrated textual-visual-geo framework for situational awareness during built environment disruptions[J]. Journal of Management in Engineering, 2020, 36(3): 04020002. 68 Lopez-Fuentes L, Farasin A, Zaffaroni M, et al. Deep learning models for road passability detection during flood events using social media data[J]. Applied Sciences, 2020, 10(24): 8783. 69 Singh J P, Dwivedi Y K, Rana N P, et al. Event classification and location prediction from tweets during disasters[J]. Annals of Operations Research, 2019, 283(1): 737-757. 70 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. 71 杨欣谊, 王伟, 朱恒民. 基于时序共词网络的社交平台话题检测与演化研究[J]. 情报学报, 2023, 42(5): 585-597. 72 Endsley M R. Designing for situation awareness in complex systems[C]// Proceedings of the Second International Workshop on Symbiosis of Humans, Artifacts and Environment, 2001: 176-190. 73 Xie W, Zhu F D, Jiang J, et al. TopicSketch: real-time bursty topic detection from Twitter[C]// Proceedings of the 2013 IEEE 13th International Conference on Data Mining. Piscataway: IEEE, 2013: 837-846. 74 Maurya A, Murray K, Liu Y D, et al. Semantic scan: detecting subtle, spatially localized events in text streams[OL]. (2016-02-13). http://arxiv.org/pdf/1602.04393. 75 Yao F, Wang Y. Towards resilient and smart cities: a real-time urban analytical and geo-visual system for social media streaming data[J]. Sustainable Cities and Society, 2020, 63: 102448. 76 Hswen Y, Yom-Tov E, Murti V, et al. Covidseeker: a geospatial temporal surveillance tool[J]. International Journal of Environmental Research and Public Health, 2022, 19(3): 1410. 77 Thapen N, Simmie D, Hankin C, et al. DEFENDER: detecting and forecasting epidemics using novel data-analytics for enhanced response[J]. PLoS One, 2016, 11(5): e0155417. 78 董青岭. 大数据安全态势感知与冲突预测[J]. 中国社会科学, 2018(6): 172-182. 79 王明程, 李勇男. 面向国家安全风险的情报感知理论模型构建研究[J]. 情报杂志, 2024, 43(1): 106-113, 76. |
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