In an ever-changing world,where the frequency and intensity of natural and humanmade disasters are on the rise,disaster risk reduction has emerged as a crucial focal point of interdisciplinary research,governance,and ...In an ever-changing world,where the frequency and intensity of natural and humanmade disasters are on the rise,disaster risk reduction has emerged as a crucial focal point of interdisciplinary research,governance,and public discourse.Disaster risk reduction,which aims to safeguard humans and protect environments from hazards and threats,is of high societal relevance and closely related to several of the United Nations Sustainable Development Goals(SDGs).The findings from research into disaster risk reduction contribute significantly to making cities and other settlements more inclusive,safe,resilient,and sustainable.展开更多
Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiote...Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event.Primarily,tweets containing information about the flooding events and its impacts were analysed.Examination of the relationships between tweet volume and its content with precipitation amount,damage extent,and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages.However,only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event.By providing insight into the quality of social media data and its usefulness to emergency management activities,this study contributes to the literature on quality of big data.Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.展开更多
文摘In an ever-changing world,where the frequency and intensity of natural and humanmade disasters are on the rise,disaster risk reduction has emerged as a crucial focal point of interdisciplinary research,governance,and public discourse.Disaster risk reduction,which aims to safeguard humans and protect environments from hazards and threats,is of high societal relevance and closely related to several of the United Nations Sustainable Development Goals(SDGs).The findings from research into disaster risk reduction contribute significantly to making cities and other settlements more inclusive,safe,resilient,and sustainable.
基金funded partially by the National Science Foundation[grant no CMMI-1335187]the Department of Homeland Security Contract[grant no HSHQDC-12-C-00057]the 2014,2015,and 2016 Arthell Kelley Scholarships from the Department of Geography and Geology at The University of Southern Mississippi.
文摘Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event.Primarily,tweets containing information about the flooding events and its impacts were analysed.Examination of the relationships between tweet volume and its content with precipitation amount,damage extent,and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages.However,only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event.By providing insight into the quality of social media data and its usefulness to emergency management activities,this study contributes to the literature on quality of big data.Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.
