A path forward for leveraging social media to improve the study of community resilience

Rachunok, Benjamin; Bennett, Jackson; Flage, Roger; Nateghi, Roshanak

doi:10.1016/j.ijdrr.2021.102236

Cited by 15 publications

(7 citation statements)

References 59 publications

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“…Li et al ( 2020 ) proposed a novel and quantitative method for studying community resilience from both mental and behavioral perspectives via sentiment and behavioral analyses during power outages. Rachunok et al ( 2021 ) describe how social media can clarify and evaluate community resilience by creating datasets based on flexible temporal and spatial scales. Wang et al ( 2021 ) found that communities with more disaster-related Twitter usage were more resilient to disasters.…”

Section: Related Workmentioning

confidence: 99%

Social media-based urban disaster recovery and resilience analysis of the Henan deluge

Shan

Zhao

2023

Nat Hazards

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Measuring disaster resilience from the perspective of long-term recovery ability is important for the planning and construction of urban sustainability, whereas short-term resilient recovery can better reflect a city’s ability to recover quickly after a disaster occurs. This study proposes an analytical framework for urban disaster recovery and resilience based on social media data that can analyze short-term disaster recovery and assess disaster resilience from the perspectives of infrastructure and people’s psychological states. We consider the downpour in Henan, China, in July 2021. The results show that (1) social media data can effectively reflect short-term disaster recovery, (2) disaster resilience can be assessed using social media data combined with rainfall and damage data, and (3) the framework can quantitatively reflect the differences in disaster recovery and resilience across regions. The findings can facilitate better decision-making in disaster emergency management for precise and effective post-disaster reconstruction and psychological intervention, and provide references for cities to improve disaster resilience.

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Section: Related Workmentioning

confidence: 99%

Social media-based urban disaster recovery and resilience analysis of the Henan deluge

Shan

Zhao

2023

Nat Hazards

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“…The use of hashtags is related to the domain of the subject, and its understanding can also vary according to time and may or may not make sense during a period [24]. For example, in the evolution of computers in the 80s and 90s, the term used to describe the technological device was microcomputer, which over time came to be called just computer, both referring to the personal computer [31]. Therefore, examples of tweets: "We used to use computers to program in Basic in the 80s: https://youtu.be/seM9SqTsRG4 #microcomputer" or "The personal computer is being replaced by smartphones: https://theuktime.com/ smartphones-replace-laptops-and-pcs/".…”

Section: Leakage Of Engagementmentioning

confidence: 99%

A Metrics-Based Approach to Metadata Classification Applied in a Recommendation System

Santos

Vales

Junior

2023

Preprint

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In this article we have presented a classification model for metadata extracted from tweets using collaborative filters, proposing a metadata recommendation system at the knowledge level. Due to the limited resources available for tagging in social media, the quality of metadata used for this practice often poses interpretation issues when selecting, resulting in a loss of engagement due to flaws in term formation or a lack of understanding of the shared metadata within the social network. In this study, we proposed a selection algorithm that filters and classifies metadata based on metrics that measure the collective intelligence aggregated in the metadata. We then index knowledge indexes that are readily identifiable visually. The proposed model involves two main steps for classifying and recommending metadata.

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“…The public crisis domain stands as a pivotal area of investigation in linguistic research [33][34][35][36]. Many researchers utilize social media data, as a medium of language, to detect crises [37][38][39], analyze resilience [40][41][42][43][44], gauge situational awareness [29,45,46], and define crisis thresholds [47,48]. A foundational step in these studies involves extracting and identifying microblogs related to rainfall and flood, typically using two approaches: bagof-words filtering and machine learning-based annotation prediction.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Urban Linguistic Diversity Related to Rainfall and Flood across China with Social Media Data

Qian,

Du,

Liang

et al. 2024

IJGI

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Understanding the public’s diverse linguistic expressions about rainfall and flood provides a basis for flood disaster studies and enhances linguistic and cultural awareness. However, existing research tends to overlook linguistic complexity, potentially leading to bias. In this study, we introduce a novel algorithm capturing rainfall and flood-related expressions, considering the relationship between precipitation observations and linguistics expressions. Analyzing 210 million social media microblogs from 2017, we identified 594 keywords, 20 times more than usual manually created bag-of-words. Utilizing Large Language Model, we categorized these keywords into rainfall, flood, and other related terms. Semantic features of these keywords were analyzed from the viewpoint of popularity, credibility, time delay, and part-of-speech, finding rainfall-related terms most common-used, flood-related keywords often more time delayed than precipitation, and notable differences in part-of-speech across categories. We also assessed spatial characteristics from keyword and city-centric perspectives, revealing that 49.5% of the keywords have significant spatial correlation with differing median centers, reflecting regional variations. Large and disaster-impacted cities show the richest expression diversity for rainfall and flood-related terms.

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A path forward for leveraging social media to improve the study of community resilience

Cited by 15 publications

References 59 publications

Social media-based urban disaster recovery and resilience analysis of the Henan deluge

Social media-based urban disaster recovery and resilience analysis of the Henan deluge

A Metrics-Based Approach to Metadata Classification Applied in a Recommendation System

Quantifying Urban Linguistic Diversity Related to Rainfall and Flood across China with Social Media Data

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