Twitter and Disasters: A Social Resilience Fingerprint

Rachunok, Benjamin; Bennett, Jackson; Nateghi, Roshanak

doi:10.1109/access.2019.2914797

Cited by 18 publications

(12 citation statements)

References 35 publications

(49 reference statements)

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“…The concept of resilience was first applied to the field of ecology [18]. With the continuous development of this idea, its field of application has gradually expanded to other disciplines, such as psychology [19], sociology [20], economics [21], etc. Recent studies have shown that the concept and definition of resilience can also be applied to transmission networks composed of nodes, links, and transmission services connecting them [22].…”

Section: Economic Factormentioning

confidence: 99%

Assessment of the Resilience of a Complex Network for Crude Oil Transportation on the Maritime Silk Road

et al. 2020

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Section: Economic Factormentioning

confidence: 99%

Assessment of the Resilience of a Complex Network for Crude Oil Transportation on the Maritime Silk Road

et al. 2020

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“…Previous research into environmental disasters has shown evidence that sourcing community generated information about local action from social media and crowd-sourcing platforms is possible [23][24][25][26], and has been employed as a live data source in several natural disasters [27,28]. Posthoc analysis has also revealed that useful data can be drawn from these sources [23,29,30], including levels of community resilience [31]. These findings show simultaneously the power of the internet for connecting people and understanding the workings of communities, and subsequently the potentially dire consequences of digital exclusion that exacerbates the lack of available support for those who are most in need [32].…”

Section: Introductionmentioning

confidence: 99%

Mapping Population Vulnerability and Community Support during COVID-19

Cara

Song

Maggio

et al. 2021

IJPDS

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Background Disasters such as the COVID-19 pandemic pose an overwhelming demand on resources that cannot always be met by official organisations. Limited resources and human response to crises can lead members of local communities to turn to one another to fulfil immediate needs. This spontaneous citizen-led response can be crucial to a community’s ability to cope in a crisis. It is thus essential to understand the scope of such initiatives so that support can be provided where it is most needed. Nevertheless, quickly developing situations and varying definitions can make the community response challenging to measure. Aim To create an accessible interactive map of the citizen-led community response to need during the COVID-19 pandemic in Wales, UK that combines information gathered from multiple data providers to reflect different interpretations of need and support. Approach We gathered data from a combination of official data providers and community-generated sources to create 14 variables representative of need and support. These variables are derived by a reproducible data pipeline that enables flexible integration of new data. The interactive tool is available online (www.covidresponsemap.wales) and can map available data at two geographic resolutions. Users choose their variables of interest, and interpretation of the map is aided by a linked bee-swarm plot. Discussion The novel approach we developed enables people at all levels of community response to explore and analyse the distribution of need and support across Wales. While there can be limitations to the accuracy of community-generated data, we demonstrate that they can be effectively used alongside traditional data sources to maximise the understanding of community action. This adds to our overall aim to measure community response and resilience, as well as to make complex population health data accessible to a range of audiences. Future developments include the integration of other factors such as well-being.

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“…Volunteer and governmental agencies need timely and credible information to save lives and get access to the people affected [ 2 ]. As such, social media has become an important tool to partially understand the social perspectives and needs before, during, and after a disaster [ 3 , 4 ], and to extract first-hand information about the phases of the event.…”

Section: Introductionmentioning

confidence: 99%

Content-based user classifier to uncover information exchange in disaster-motivated networks

et al. 2021

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Disasters strike communities around the world, with a reduced time-frame for warning and action leaving behind high rates of damage, mortality, and years in rebuilding efforts. For the past decade, social media has indicated a positive role in communicating before, during, and after disasters. One important question that remained un-investigated is that whether social media efficiently connect affected individuals to disaster relief agencies, and if not, how AI models can use historical data from previous disasters to facilitate information exchange between the two groups. In this study, the BERT model is first fine-tuned using historical data and then it is used to classify the tweets associated with hurricanes Dorian and Harvey based on the type of information provided; and alongside, the network between users is constructed based on the retweets and replies on Twitter. Afterwards, some network metrics are used to measure the diffusion rate of each type of disaster-motivated information. The results show that the messages by disaster eyewitnesses get the least spread while the posts by governments and media have the highest diffusion rates through the network. Additionally, the “cautions and advice” messages get the most spread among other information types while “infrastructure and utilities” and “affected individuals” messages get the least diffusion even compared with “sympathy and support”. The analysis suggests that facilitating the propagation of information provided by affected individuals, using AI models, will be a valuable strategy to pursue in order to accelerate communication between affected individuals and survival groups during the disaster and aftermath.

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Twitter and Disasters: A Social Resilience Fingerprint

Cited by 18 publications

References 35 publications

Assessment of the Resilience of a Complex Network for Crude Oil Transportation on the Maritime Silk Road

Assessment of the Resilience of a Complex Network for Crude Oil Transportation on the Maritime Silk Road

Mapping Population Vulnerability and Community Support during COVID-19

Content-based user classifier to uncover information exchange in disaster-motivated networks

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