A review of social media use during disaster response and recovery phases

Young, Camila Espina; Kuligowski, Erica D.; Pradhan, Aashna

doi:10.6028/nist.tn.2086

Cited by 17 publications

(9 citation statements)

References 53 publications

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“…The real-time publishing of information on these platforms means that in disaster situations users act as human sensors, detecting and documenting events. Social Media has already been implemented to detect and predict aspects of influenza spread [13], social unrest [14,20], polling and election election outcomes [21], mental illness [22] and addiction [23], as well as general natural disasters [11,24] and finally wildfires [8,25]. Studies related to disaster scenarios have revealed interesting results, building on the concept of the human sensor to inform decision making and recovery in these situations.…”

Section: The Human Sensormentioning

confidence: 99%

“…(C) We train models which accurately predict attributes of the novel dataset. Accurate modelling social media sentiment during disasters may help disaster management and recovery crews make more informed, quick, data-driven decisions [11,17,24,26,33]. This is a key aspect of wildfire modelling which needs improvement, and could help fire suppression and reduce public danger.…”

Section: Sentimental Wildfire: Overviewmentioning

confidence: 99%

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Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

Lever

Arcucci

2022

J Comput Soc Sc

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The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.

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Section: The Human Sensormentioning

confidence: 99%

Section: Sentimental Wildfire: Overviewmentioning

confidence: 99%

Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

Lever

Arcucci

2022

J Comput Soc Sc

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“…In humanitarian logistics and disaster management, typical decision-making problems rely on the humanitarian logistics operations associated with the disaster life cycle, or disaster management cycle, which is a framework popularly utilised in disaster management to understand, visualise and delineate the distinct stages of a developing disaster event. 'Its main purpose is to tie the temporal dimension of an emergency with the appropriate functions for its successful management' (Young et al 2020). Diverse authors summarise the disaster management cycle in two phases only (e.g.…”

Section: Optimisation and Decision-making In Humanitarian Logisticsmentioning

confidence: 99%

Insights from vulnerability-driven optimisation for humanitarian logistics

Alem¹

2021

JBA

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Natural disasters and the vulnerability of a population go hand in hand. We cannot understand the level of a disaster without grasping the extent of people�s vulnerability. But how can we ensure that humanitarian assistance is driven by people�s vulnerability when the lack of resources makes it impossible to support all those that need it? This study thus contributes to this line of research by enhancing our understanding of how we can �put the reality of the most vulnerable people first� (cf. Chambers 1995). For this purpose, we examine two experiences that have proposed to incorporate vulnerability concerns into the planning and optimisation of humanitarian logistics operations. The first experience relies on a very popular composite indicator called Social Vulnerability Index (SoVI) to build enhanced response capacity in more vulnerable areas. The second experience is built upon a poverty measure called Foster-Greer-Thorbecke (FGT) to identify the groups that potentially need the most relief aid supply and to help devising allocation plans in compliance with people�s income. These two experiences reveal that in most cases targeting more vulnerable areas increases their level of access to relief aid goods without greatly compromising the relief service levels of less vulnerable areas.

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“…A review capturing the strengths and weaknesses of the literature on disaster mental health monitoring via social media is both pertinent and timely, given the availability of social media analytic tools and the current COVID-19 crisis [ 9 ]. Previous reviews [ 10 , 11 ] have taken a narrower focus by examining the health literature; however, substantial research has been published in other interdisciplinary areas [ 12 ]. Notably, research from computer science and engineering is particularly relevant, and may offer sophisticated methodological advances to address challenges specific to large, unstructured data sets obtained from social media to indicate mental health outcomes [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review

Teague¹,

Shatte²,

Weller³

et al. 2022

JMIR Ment Health

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Background With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. Objective This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. Methods A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. Results Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. Conclusions The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media–based disaster mental health monitoring, such as combining surveillance with social media–based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters.

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A review of social media use during disaster response and recovery phases

Cited by 17 publications

References 53 publications

Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

Insights from vulnerability-driven optimisation for humanitarian logistics

Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review

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