Carmen De Maio, Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, Alberto Volpe
{"title":"A Perceived Risk Index Leveraging Social Media Data: Assessing Severity of Fire on Microblogging","authors":"Carmen De Maio, Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, Alberto Volpe","doi":"10.1007/s12559-024-10266-4","DOIUrl":null,"url":null,"abstract":"<p>Fires represent a significant threat to the environment, infrastructure, and human safety, often spreading rapidly with wide-ranging consequences such as economic losses and life risks. Early detection and swift response to fire outbreaks are crucial to mitigating their impact. While satellite-based monitoring is effective, it may miss brief or indoor fires. This paper introduces a novel Perceived Risk Index (PRI) that, complementing satellite data, leverages social media data to provide insights into the severity of fire events. In the light of the results of statistical analysis, the PRI incorporates the number of fire-related tweets and the associated emotional expressions to gauge the perceived risk. The index’s evaluation involves the development of a comprehensive system that collects, classifies, annotates, and correlates social media posts with satellite data, presenting the findings in an interactive dashboard. Experimental results using diverse datasets of real-fire tweets demonstrate an average best correlation of 77% between PRI and the brightness values of fires detected by satellites. This correlation extends to the real intensity of the corresponding fires, showcasing the potential of social media platforms in furnishing information for emergency response and decision-making. The proposed PRI proves to be a valuable tool for ongoing monitoring efforts, having the potential to capture data on fires missed by satellites. This contributes to the development to more effective strategies for mitigating the environmental, infrastructural, and safety impacts of fire events.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Computation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12559-024-10266-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Fires represent a significant threat to the environment, infrastructure, and human safety, often spreading rapidly with wide-ranging consequences such as economic losses and life risks. Early detection and swift response to fire outbreaks are crucial to mitigating their impact. While satellite-based monitoring is effective, it may miss brief or indoor fires. This paper introduces a novel Perceived Risk Index (PRI) that, complementing satellite data, leverages social media data to provide insights into the severity of fire events. In the light of the results of statistical analysis, the PRI incorporates the number of fire-related tweets and the associated emotional expressions to gauge the perceived risk. The index’s evaluation involves the development of a comprehensive system that collects, classifies, annotates, and correlates social media posts with satellite data, presenting the findings in an interactive dashboard. Experimental results using diverse datasets of real-fire tweets demonstrate an average best correlation of 77% between PRI and the brightness values of fires detected by satellites. This correlation extends to the real intensity of the corresponding fires, showcasing the potential of social media platforms in furnishing information for emergency response and decision-making. The proposed PRI proves to be a valuable tool for ongoing monitoring efforts, having the potential to capture data on fires missed by satellites. This contributes to the development to more effective strategies for mitigating the environmental, infrastructural, and safety impacts of fire events.
期刊介绍:
Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.