Characterizing reticulation in online social networks during disasters

Fan, Chao; Shen, Jiayi; Mostafavi, Ali; Hu, Xia

doi:10.1007/s41109-020-00271-5

Cited by 7 publications

(8 citation statements)

References 47 publications

(48 reference statements)

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“…Yang et al 18 investigated influential Twitter users during Hurricane Harvey, highlighting the role of objective information sharing in disaster-related follower growth. Fan et al 13 studied dynamic changes in network structure during information diffusion of disaster information on social networks. Dong et al 19 created an information flow model from Weibo data to characterize information diffusion patterns during disasters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous studies have explored the evolution of disaster information on online social networks like Twitter and Facebook 12 , 13 , but little work exists for face-to-face networks or other offline communication methods. Our approach investigates the complex spread of information in disaster-affected communities by analyzing the interplay between five information sources: online social networks, local news, government sources, mainstream media, and print media.…”

Section: Introductionmentioning

confidence: 99%

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Towards quantifying the communication aspect of resilience in disaster-prone communities

Okeukwu-Ogbonnaya,

Amariucai,

Natarajan

et al. 2024

Sci Rep

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In this study, we investigate the communication networks of urban, suburban, and rural communities from three US Midwest counties through a stochastic model that simulates the diffusion of information over time in disaster and in normal situations. To understand information diffusion in communities, we investigate the interplay of information that individuals get from online social networks, local news, government sources, mainstream media, and print media. We utilize survey data collected from target communities and create graphs of each community to quantify node-to-node and source-to-node interactions, as well as trust patterns. Monte Carlo simulation results show the average time it takes for information to propagate to 90% of the population for each community. We conclude that rural, suburban, and urban communities have different inherent properties promoting the varied flow of information. Also, information sources affect information spread differently, causing degradation of information speed if any source becomes unavailable. Finally, we provide insights on the optimal investments to improve disaster communication based on community features and contexts.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards quantifying the communication aspect of resilience in disaster-prone communities

Okeukwu-Ogbonnaya,

Amariucai,

Natarajan

et al. 2024

Sci Rep

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“…When analyzing sentiment expressions on Twitter during Hurricane Harvey, Yuan et al [34] found that the impacted areas were more likely to indicate negative sentiment during disasters. Hence, the content of social media communications could shed insight on the dimension of human activities [35].…”

Section: A Community Realitymentioning

confidence: 99%

Unraveling the Temporal Importance of Community-Scale Human Activity Features for Rapid Assessment of Flood Impacts

Yuan

Yang

et al. 2022

IEEE Access

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The objective of this research is to explore the temporal importance of community-scale human activity features for rapid assessment of flood impacts. Ultimate flood impact data, such as flood inundation maps and insurance claims, becomes available only weeks and months after the floods have receded. Crisis response managers, however, need near-real-time data to prioritize emergency response. This time lag creates a need for rapid flood impact assessment. Accordingly, community-scale big data (such as satellite imagery) has been utilized for the early estimation of end flood impacts. Some recent studies have shown promising results for using human activity fluctuations as indicators of flood impacts. Existing studies, however, used mainly a single community-scale activity feature for the estimation of flood impacts and have not investigated their temporal importance for indicating flood impacts. Hence, in this study, we examined the importance of heterogeneous human activity features (such as human mobility, visits to points-of-interest, and social media posts) in different flood event stages. Using four community-scale big data categories we derived ten features related to the variations in human activity (e.g., travel, credit card transactions, and online communications) and evaluated their temporal importance for rapid assessment of flood impacts. Using multiple random forest models, we examined the temporal importance of each feature in indicating the extent of flood impacts (measured by the flood insurance claims and flood inundations) in the context of the 2017 Hurricane Harvey in Harris County, Texas. Our findings reveal that 1) fluctuations in human activity index and percentage of congested roads are the most important indicators for rapid flood impact assessment during response and recovery stages; 2) variations in credit card transactions assumed a middle ranking in both response and recovery stages; and 3) patterns of geolocated social media posts (Twitter) were of low importance across flood stages. Insights derived from data analysis reveal the potential for harnessing community-scale data characterizing human activity fluctuations for rapid assessment of flood impacts. The results of this research could rapidly forge a multi-tool enabling crisis managers to identify hotspots with severe flood impacts at various stages then to plan and prioritize effective response strategies.INDEX TERMS Smart resilience, urban flood, machine learning, big data, rapid flood impact assessment.

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“…Hence, the content of social media communications could shed insight on the dimension of human activities. (Fan et al 2020c).…”

Section: Community Realitymentioning

confidence: 99%

Unraveling the Temporal Importance of Community-scale Human Activity Features for Rapid Assessment of Flood Impacts

Yuan¹,

Yang²,

Li³

et al. 2021

Preprint

Self Cite

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The objective of this research is to explore the temporal importance of community-scale human activity features for rapid assessment of flood impacts. Ultimate flood impact data, such as flood inundation maps and insurance claims, becomes available only weeks and months after the floods have receded. Crisis response managers, however, need near-real-time data to prioritize emergency response. This time lag creates a need for rapid flood impact assessment. Accordingly, communityscale big data (such as satellite imagery) has been utilized for the early estimation of end flood impacts. Some recent studies have shown promising results for using human activity fluctuations as indicators of flood impacts. Existing studies, however, used mainly a single community-scale activity feature for the estimation of flood impacts and have not investigated their temporal importance for indicating flood impacts. Hence, in this study, we examined the importance of heterogeneous human activity features (such as human mobility, visits to points-of-interest, and social media posts) in different flood event stages. Using four community-scale big data categories we derived ten features related to the variations in human activity (e.g., travel, credit card transactions, and online communications) and evaluated their temporal importance for rapid assessment of flood impacts. Using multiple random forest models, we examined the temporal importance of each feature in indicating the extent of flood impacts (measured by the flood insurance claims and flood inundations) in the context of the 2017 Hurricane Harvey in Harris County, Texas. Our findings reveal that 1) fluctuations in human activity index and percentage of congested roads are the most important indicators for rapid flood impact assessment during response and recovery stages; 2) variations in credit card transactions assumed a middle ranking in both response and recovery stages; and 3) patterns of geolocated social media posts (Twitter) were of low importance across flood stages. Insights derived from data analysis reveal the potential for harnessing community-scale data characterizing human activity fluctuations for rapid assessment of flood impacts. The results of this research could rapidly forge a multi-tool enabling crisis managers to identify hotspots with severe flood impacts at various stages then to plan and prioritize effective response strategies.Keywords smart resilience • urban flood • machine learning • big data • rapid flood impact assessment

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Characterizing reticulation in online social networks during disasters

Cited by 7 publications

References 47 publications

Towards quantifying the communication aspect of resilience in disaster-prone communities

Towards quantifying the communication aspect of resilience in disaster-prone communities

Unraveling the Temporal Importance of Community-Scale Human Activity Features for Rapid Assessment of Flood Impacts

Unraveling the Temporal Importance of Community-scale Human Activity Features for Rapid Assessment of Flood Impacts

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