“With Your Help... We Begin to Heal”: Social Media Expressions of Gratitude in the Aftermath of Disaster

Glasgow, Kimberly; Vitak, Jessica; Tausczik, Yla R.; Fink, Clay

doi:10.1007/978-3-319-39931-7_22

Cited by 16 publications

(18 citation statements)

References 22 publications

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“…Wen and Lin [219] studied the factors (geographic proximity, media exposure, social support, and gender) of distress (anxiety, sadness, and anger) after the 2015 Paris terror attacks, and compared the immediate acute responses and the ones before the attacks. Glasgow et al [220] compared the expressions of gratitude for social support received after the 2011 Alabama tornado and the 2012 Sandy Hook school shooting (Newtown, CT), and found that, despite the Alabama victims suffering from a more severe disaster (quantitatively), they received proportionally fewer expressions of support. By examining the microblog posts (i.e., from Twitter) about the Flint water crisis, Oz and Bisgin [221] studied the attribution of responsibility and blame in a man-made disaster.…”

Section: Interactions Among the Components Of Cssdmentioning

confidence: 99%

Computational Social Science of Disasters: Opportunities and Challenges

Burger

Kennedy

et al. 2019

Future Internet

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Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

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Section: Interactions Among the Components Of Cssdmentioning

confidence: 99%

Computational Social Science of Disasters: Opportunities and Challenges

Burger

Kennedy

et al. 2019

Future Internet

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“…[264,265]); monitoring mental health following an event or disaster (e.g. [266,267]); and creating models of risk to improve health system delivery (e.g. [268,269]) (see Table 3).…”

Section: Mental Health Application ML Technique(s) Data Typementioning

confidence: 99%

“…Social media data also enables researchers to assess the impact of an incident on population mental health (e.g., stress levels of college students after experiencing gun violence [270]), and to track public response to disaster situations to inform the allocation of support resources [266,267,269]. ML applied to electronic health records was demonstrated to predict suicide risk with an accuracy similar to clinician assessment [268,271], as well as predict dementia and its risk factors with high accuracy [272].…”

Section: Mental Health Application ML Technique(s) Data Typementioning

confidence: 99%

“…Regression [281,282], DT [282], SVM [282], RF [281], Super Learner [281] Interview [282], Survey [281] Psychiatric Emergency BN [269], DT [269], SVM [269] Social Media [269] Psychiatric Stressors NLP [283], Named-entity recognition [283] Clinical Notes [283] Psychosis Regression [284], RF [285] Clinical Assessment [285], Electronic Health Records [284] Social Support LIWC [267], SVM [267] Social Media [267] Stress Cluster analysis [286], Sentiment Analysis [270], SVM [270] Clinical Assessment [286], Social Media [270] Substance Use PCA [265], NLP [265], RF [285] Social Media [265], Clinical Assessment [285] Suicide/Self-Harm ARM [271], DT [271], Genetic Algorithm [287], NB [268,271], RF [268,271], Regression [268,271,[288][289][290], SVM …”

Section: Technique(s) Data Typementioning

confidence: 99%

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Machine learning in mental health: A systematic scoping review of methods and applications

Shatte¹,

Hutchinson²,

Teague³

2018

Preprint

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ObjectiveThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. Materials and MethodsEight health and information technology research databases were searched using the terms "big data" or "machine learning" and "mental health". Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type and size, and study results. Articles were then synthesised via narrative review. ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health; and, (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's Disease. ML techniques used included support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering. Discussion and ConclusionOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration.With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field. BACKGROUND AND SIGNIFICANCE

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“…Although altruistic behaviors are common in times of disasters (Glasgow et al, 2016), in the recovery phase, when the community is pressed by difficult living conditions and when there is a lack of short-term improvement, public increase their criticism of administrators, and attribute blame to whom they perceive as the agents of responsibility. Among other psychological and social reasons, a political explanation for this act is that in democracies the public acts as a watchdog and actively participates in discussions to control and influence the decision makers.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Blame and Responsibility Through Social Media in Disaster Recovery in the Case of #FlintWaterCrisis

Havens

Bişğin

2018

Front. Commun.

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Attribution of responsibility and blame are important topics in political science especially as individuals tend to think of political issues in terms of questions of responsibility, and as blame carries far more weight in voting behavior than that of credit. However, surprisingly, there is a paucity of studies on the attribution of responsibility and blame in the field of disaster research. In this work, we investigate the attribution of responsibility and blame through social media in the case of Flint water crisis. We form hypotheses based on social scientific theories in disaster research and then operationalize them on public responses available on social media rather than employing traditional data collection methods such as interviewing and surveying. In particular, we investigate the source for blame, the partisan predisposition, the concerned geographies, and the contagion of complaining by testing our hypotheses on data collected from Twitter. Our results demonstrate the utility of social media data in testing those hypotheses, which are rooted in sociology of disasters. Our findings are not only aligned with official reports listing the responsible officials for the source blame, but also reveal a partisan predisposition in regards to Democratic and Republican stances. We also confirm that closer geographies are more concerned and complaining seems contagious in social media conversations. This paper adds to the sociology of disasters research by exploiting a new, rarely used data source (the social web), and by employing new computational methods (such as sentiment analysis and retrospective cohort study design) on this new form of data. In this regard, this work can be seen as the first step toward drawing more challenging inferences on the sociology of disasters from "big social data".

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“With Your Help... We Begin to Heal”: Social Media Expressions of Gratitude in the Aftermath of Disaster

Cited by 16 publications

References 22 publications

Computational Social Science of Disasters: Opportunities and Challenges

Computational Social Science of Disasters: Opportunities and Challenges

Machine learning in mental health: A systematic scoping review of methods and applications

Assessment of Blame and Responsibility Through Social Media in Disaster Recovery in the Case of #FlintWaterCrisis

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